Research on deep neural network optimization of financial education knowledge base

This exploration activity focuses on the use of deep neural network technology to refine and optimize the financial education knowledge base, which in turn greatly improves the accuracy of information retrieval and realizes the real-time updating and synchronization mechanism of the information content. On the occasion of analyzing the huge amount of financial data, the Transformer model architecture is adopted, which integrates the self-attention mechanism and the multi-attention technology to achieve the high efficiency of data processing. A comprehensive and detailed deep mining of financial data is carried out, and more than one hundred rounds of iterative training phases are completed. The model's error level in the training and validation phases is significantly reduced, and the accuracy level realizes a leap of 97%, and the empirical analysis reveals that the model shows excellent analytical strength in the field of financial analysis, and that the model possesses the remarkable feature of responding quickly to specific events in the financial field, and this experimental result The experimental results prove the feasibility of its practical application in the field of real-time data processing, and the introduction of deep neural network technology has a significant positive effect on the functionality of the knowledge base of financial education, and also provides a strong support for the continuous progress of financial data processing technology.