1. Introduction

With the rapid development of digital information and the popularization of the Internet, some of the data generated by enterprises in their daily operations, including production and sales, has increased exponentially. At the same time, today's market competition is becoming increasingly fierce, enterprises need to be able to more accurately insight into the market and customer needs, formulate targeted and forward-looking development planning, and provide reliable data support and decision-making reference for the development planning of enterprises. At present, researchers have begun to use machine learning technology to conduct enterprise development planning based on big data, which includes market trend analysis, business behavior prediction of competitors, customer demand prediction and other aspects of research. However, most current research is based on static data for enterprise development planning and does not consider dynamic data changes. How to use machine learning technology to build uncertainty modeling and establish dynamic programming models to deal with market uncertainty and variability is the direction and problem machine learning needs to develop in the future. The significance of this study lies in the rationalization and systematic use of machine learning technology to improve the flexibility of enterprise planning .

2. Basic principles and key technologies of machine learning

2.1. Basic principles and methods of machine learning

Machine learning is based on computer operation and combined with information recognition technology, which can automatically analyze and screen data groups with potential value[1]. Machine learning refers to the continuous learning and processing of data given by humans without explicit programming regulations, the analysis of regular information in massive data, the continuous training of models in the given data to improve intelligence, and the learning of the model to make it more and more accurate. Thus, it can help human beings make more reliable judgments or predictions on the data that may appear in the future, and comb analyze, summarize and summarize the existing data. Its basic purpose is to use algorithms and statistical models to automatically identify features and trends in data and generate prediction results or make decisions on future data based on these identified features and trends. Machine learning is an automated process that enables the prediction and classification of unknown data through the learning and analysis of large amounts of data[2].

Figure 1. Machine learning algorithm in action [3]

Figure 1 shows the flow of machine learning and illustrates some types of machine learning. As shown in Figure 1, machine learning methods are divided into three paradigms: supervised, unsupervised and reinforcement learning. In supervised learning, the model learns through labeled training data. In unsupervised learning, the model learns hidden structures and rules from unlabeled data. In reinforcement learning, the model learns optimal behavioral strategies by interacting with the environment. It can be divided into classification, regression, decision tree, clustering, deep learning, etc., according to the similarity of form or function. However, no matter which type, it is basically analyzing the possible data or trends through the given training samples. For different data and needs, we can choose different types of machine learning to process data. Figure 2 shows the interpretability and accuracy of different machine learning algorithms, illustrating the different interpretability and accuracy of different machine algorithms.

Figure 2. Interpretability and accuracy of different machine learning algorithms [3]

2.2. Overview of Key Technologies

One of the key techniques in the field of machine learning is reinforcement learning. The reinforcement learning algorithm is a kind of machine learning algorithm that continuously tries and makes mistakes through machine intelligence and environment interaction, and finally learns to optimize behavioral strategies to finally achieve the goal of obtaining maximum benefits. Reinforcement learning algorithms are often used in environments that are constantly interacting, such as games, robot control, and autonomous driving [2].

Another key technology is deep learning. Deep learning, a variant of reinforcement learning, is a machine learning approach based on artificial neural networks, the core idea is to autonomously learn large amounts of data through multi-layered neural network structures. Deep learning has made remarkable achievements in image recognition, speech recognition, natural language processing and other fields.

It solves the problem of continuous action space and high dimensional state space by constructing a neural network. Compared with traditional reinforcement learning algorithms, deep reinforcement learning can independently learn the mapping relationship between states and actions without manually designing features, thus applicable to a wider range of problem domains [2].

3. Application status of machine learning in enterprise development planning

3.1. Application of machine learning in the development of corporate finance (Market forecasting and analysis)

The application of machine learning in the development of corporate finance has a wide range of applications in market forecasting and analysis, which can help enterprises extract useful information from massive data, discover potential trends, and make more accurate decisions. In recent years, more enterprises have widely applied machine learning as a financial prediction model. Compared with traditional statistical analysis, machine learning has higher accuracy, efficiency and less manpower input [4]. With the development of this field, research has gradually expanded, and more machine learning models and algorithms have emerged.

A concrete example is the application of AlphaSense, an American fintech company. AlphaSense uses natural language processing and machine learning technologies to help financial institutions analyze massive amounts of unstructured data such as financial reports, social news, social media comments, research reports, and more to provide deeper market insights and predictive capabilities. AlphaSense's machine learning algorithms can quickly and accurately analyze large amounts of textual data, extract key information, and identify important trends and events. This information can help financial practitioners better understand market dynamics, formulate investment strategies and manage risks. Specifically, AlphaSense's machine learning technology can be used for market sentiment analysis, company performance forecasting, and industry trend analysis. Through AlphaSense's machine learning technology, financial institutions can better utilize big data for market forecasting and analysis, improve the accuracy and efficiency of investment decisions, and thus promote the development of corporate finance.

Another example is Quantopian, a company that provides a quantitative investment platform for investors. They use machine learning and big data analytics to help investors with market forecasting and analysis. Quantopian's platform allows users to access vast amounts of financial data and leverage machine learning algorithms to spot patterns and trends in the market. By analysing these market financial data through machine learning technology, investors can better understand market dynamics, grasp the direction of market finance, and develop more effective trading strategies.

Kensho Technologies also uses machine learning in the financial field to help it make efficient and accurate decisions. It is an artificial intelligence and machine learning company focused on the financial industry. They have developed various tools based on natural language processing and machine learning to analyze financial market and economic data to provide more effective strategies. Kensho's technology helps investors quickly access and understand large amounts of market information to make more accurate decisions. Their products cover everything from stock price forecasting to analysis of the impact of macroeconomic events.

From the above examples, the application of machine learning in the development of enterprise finance, especially in market prediction and analysis, has great potential and important significance. By leveraging machine learning technologies, companies can better understand market dynamics, identify potential opportunities, and make more accurate and timely decisions that drive their financial development and innovation.

3.2. Application of machine learning in enterprise economic development planning (strategic planning and decision making)

With the digital economy leading the transformation and upgrading of listed companies in all walks of life and the outbreak of the epidemic in early 2020, the difficulty of enterprise economic management has increased [4]. Therefore, enterprises' demand for economic management efficiency and accuracy is constantly increasing.

Enterprises can use machine learning technology to analyze market data, consumer behavior, competitor dynamics and other information to gain insight into market trends and future development direction, and adjust their economic development plans. Based on the analysis of customer data, enterprises can realize personalized marketing and service, improve customer satisfaction and loyalty, improve customer buyback rate, and thus enhance market competitiveness. Based on the analysis and pattern recognition of a large amount of data, machine learning can help enterprises predict market demand, product popularity, and changes in the competitive landscape, so as to guide the strategic planning and product positioning of enterprises, and reasonably plan the production plan of products. Enterprises can also use machine learning algorithms to optimize supply chain and human resource management, helping enterprises optimize supply chain and personnel recruitment processes. At the same time, machine learning can help enterprises identify potential risk factors and provide intelligent decision support. By analysing historical data and market dynamics, machine learning can help enterprises predict the probability and possible impact of risk events and formulate corresponding risk management strategies. Odom et al. were the first to use an artificial neural network model as an early warning model. Liu Hong and He Guangjun used Fisher discrimination, Logistic regression and BP neural network analysis methods to establish models, and experiments proved that BP neural network relied on nonlinear functions to build prediction models better and had good early warning effects [3].

In summary, by utilizing machine learning technology, enterprises can better understand market dynamics, optimize resource allocation, and improve decision-making efficiency and corporate competitiveness, thus achieving sustainable economic development.

3.3. Application of machine learning in enterprise marketing strategy (strategic planning and decision making)

Market segmentation is an indispensable link in marketing strategy in traditional marketing practice. Usually, enterprises will send marketing personnel with certain market experience to issue questionnaires with reasonable questions to understand the real situation of the market and find the market segmentation standards suitable for the actual situation of the enterprise. However, with the development of machine learning technology, the use of machine learning for analysis can be better than the need for experienced marketers to use the transaction data stored in the database to accurately describe the characteristics of target consumers [5].

The application of machine learning in enterprise marketing strategy also covers many aspects, such as analyzing user purchasing behavior, browsing preferences and interest in goods through machine learning algorithms, enterprises can realize personalized product recommendations to users. Machine learning can also help companies precisely target potential customers based on their profiles, interests, and behavioral characteristics, and deliver ads accordingly. By analyzing market data, consumer behavior, and competitor dynamics, machine learning can help companies predict future market demand, product popularity, and changes in the competitive landscape of products and businesses.

The application of machine learning in marketing is becoming more and more extensive with the development of machine learning technology. Amazon is an example of a company that uses machine learning to optimize its marketing strategy. They use machine learning algorithms to analyze a user's purchase history, browsing behavior, and search history to provide personalized product recommendations and ads to each user. Through targeted advertising and personalized recommendations, Amazon can increase the purchase rate of products and improve the interests of enterprises. Netflix also extensively uses machine learning to optimize its marketing strategy. It uses machine learning algorithms to analyze users' viewing history, search history, ratings and preferences. Then recommends movies and television works that users may be interested in based on the analysis results of machine learning technology. This personalized recommendation system can increase user satisfaction and retention, increasesing subscriptions. In addition, Alibaba is also actively applying machine learning to optimize marketing strategies in e-commerce. They use machine learning algorithms to analyze users' buying behavior and preferences, and then make recommendations and marketing campaigns based on that data. Through personalized marketing strategies such as coupons and recommended products, Alibaba can attract more users and improve the conversion rate of users' purchases. Facebook, as a social media platform, uses machine learning algorithms to analyze users' social network relationships.

4. Prospects and challenges of machine learning in enterprise planning and development

4.1. Outlook (Development trend forecast)

Machine learning has a wide range of applications in the enterprise, and future development will cover many aspects. First, an intelligent decision support system will become an important tool for enterprise management to plan internal development in an orderly and reasonable way. Machine learning can be used for construction.

Build consumer profiles, predict consumer behavior, collect consumer evaluations and other marketing activities [5]. Secondly, by flexibly applying machine learning technology to customers' purchase and browsing histories, enterprises can better understand customer needs and behavior patterns, and provide customers with customized products and services. Through personalized service and recommendation systems, customers can more easily purchase the products they need or are interested in, and improve the profits of enterprises.

In addition, machine learning can also be used in risk management and security, healthcare industry enterprises, and life sciences sector enterprises for continuous innovation and development.

4.2. Challenge Analysis

First, as data accumulates and is used, businesses face privacy and security challenges. Ensuring data security and compliance, and protecting user privacy have become issues that cannot be ignored in the development of enterprises. In future research, the development of technology will drive the emergence of various new marketing models and new forms, and it is necessary to think and analyze relevant issues in advance to deal with the balance between the development of artificial intelligence marketing itself, laws and ethics, and avoid the occurrence of unintended consequences [6]. Finally, some machine learning models are less interpretive, making it challenging to explain their decision-making processes, which can reduce trust in the models. In addition, future research needs to pay special attention to the improvement and enhancement of artificial intelligence marketing on consumer well-being and develop a more diversified evaluation system rather than simply referring to a single evaluation index such as satisfaction, participation, loyalty, and purchase rate, to comprehensively analyze the actual value brought by artificial intelligence marketing to consumers and its differential impact on consumers [6].

5. Conclusion

This paper mainly studies the application status and prospect of machine learning in enterprise development planning, including the challenges faced by machine learning technology in development. Through relevant investigation and research, this paper finds that machine learning technology is widely used in enterprise development planning, brings much convenience to enterprises, and plays a huge positive role in all aspects and fields of enterprises, especially for the future development of enterprises in the planning has played a directional role. Although machine learning technology has broad prospects in enterprise applications, the development of machine learning is also accompanied by many problems and challenges, which need people to find and solve, such as some technical failures and innovation problems brought by machine learning, so that machine learning method can play a more significant role and space. This paper also has some shortcomings. Some content is a little redundant, the literature is not rich enough and so on. This paper believes that the future research direction of machine learning may focus on enterprise risk management and enterprise internal planning It is expected that machine learning technology will continue to play a positive role in various fields of enterprises, create new value for enterprise development.