Design Mechanism of Intelligent Financial Analysis System Based on Data Mining Technology and Neural Network

The proposed intelligent financial analysis system leverages data mining techniques, integrating AI and audit expertise. It includes a novel short-text classification algorithm based on a Bidirectional Long Short-Term Memory (BiLSTM) neural network. This algorithm addresses feature sparsity, high dimensionality, and inadequate semantic feature extraction typical of short text data. By combining Word2vec, TF-IDF, and LDA topic modeling, the system creates a vector that captures semantic, importance, and topic features. The BiLSTM architecture processes texts in forward and backward directions, enhancing context understanding and mitigating gradient issues associated with traditional Recurrent neural networks. The algorithm is designed for text preprocessing, vector representation, neural network construction, and classification and evaluation. This approach ensures accurate and efficient financial audit text classification, providing valuable insights and recommendations.