Research on Financial Big Data Collection and Intelligent Decision-Making System Based on Multimodal Large Language Model

With the explosive growth of data in the financial industry, efficient collection, analysis, and utilization of this data have become urgent challenges. This study aims to develop a financial big data collection and intelligent decision-making system based on multimodal large language models (MM-LLM). The system utilizes Google's AI mega-model, Gemini, to achieve functionalities such as classification, retrieval, analysis, intelligent evaluation, and recommendation of financial big data. Through parameter configuration on the management side, the system can flexibly adapt to the data collection and analysis needs in different financial scenarios, significantly improving the efficiency of data processing and the accuracy of decision-making. Innovative solutions are provided to address the specific requirements of investors in analyzing vast announcement data and evaluating individual stocks, assisting them in making wiser investment decisions. The system adopts a front-end and back-end separation architecture, based on distributed microservices, and integrates technologies such as Vue3, Springboot, Nacos, Dubbo, Redis cluster, MySQL, Elasticsearch, Gemini, Ansj, and Kafka. By developing and applying this system, the study demonstrates the vast potential of multimodal large language models in the field of financial big data, laying a solid foundation for future intelligent financial decision-making,