Research on Financial Information Management Based on Convolutional Neural Network Algorithm - Xu, Zhengqiang; Tan, Zhugang; Shi, Xiaofei; Cao, Hui; Zhang, Yang (Innovative Applications of AI, 2024)
Due to the frequent occurrence of local minimization, slow convergence speed, and inconsistent structural selection in traditional BP neural network models, it can have a certain degree of impact on the algorithm. To overcome this problem, this study Uses an convolutional neural network (CNN) algorithm model, financial data information is processed and reduced dimensionally, converting complex high-dimensional data that frequently occur into simplified and easily manageable low-dimensional data information, thus enhancing data information management capability. In order to improve the training ability of data information, this paper designs an auxiliary model tensor convolutional autoencoder neural network model to achieve the analysis and processing of multi-dimensional data in hospital finance. Among them, tensor convolutional autoencoder neural network is an auxiliary model of the main model. The main implementation of this algorithm model is the processing and analysis of multidimensional data, greatly improving the efficiency of financial data information processing and analysis. Experimental results demonstrate the effectiveness of the proposed method, achieving fault diagnosis and comprehensive management of financial data. From the perspectives of storage and traceability of financial information, a new model for enterprise financial information management is established, providing insights for the specific applications of blockchain in enterprise financial information management. However, the research conducted in this study is only an exploratory analysis of the integration of blockchain and enterprise financial information management, and further specific analysis is required to address more practical issues in real-world applications.