Simulation of regional financial behavior patterns based on recommendation algorithm and machine learning

The rapid growth of regional economy can effectively solve the financial difficulties of some large enterprises, and can also provide important guarantees for the sustainable economic development of some regions. In modern financial regional entities, commercial banks, conglomerates, and third-party technology companies are the main modules. Financial development issues in different regions need to be addressed through the design of solutions. In the cross domain and even cross border activities of regional economy, it is necessary to integrate some existing financial tools, as well as financial services such as big data and digital technology, to integrate. In the construction of a new regional financial model, regional financial health is a major development issue, and it is necessary to take dredging regional financial development as an important field to promote the rapid development of the real economy. In recent years, learning in natural language processing has achieved cross era development. With the growth of the complexity and dynamics of network information, computer algorithms have become an important key issue in solving network information. Although high-quality recommendation algorithms have been widely used, in order to solve problems that traditional algorithms cannot solve, this paper proposes a machine learning based algorithm based on the needs of financial entities. At the same time, in order to enable the rapid development of finance, there is a new development model that conducts behavioral simulation calculations based on economic development in order to achieve coordinated economic development.