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In this paper, a cascaded depth learning framework is constructed. A cascaded depth model is successfully implemented by studying and analyzing the specific feature transformation, feature selection, and classifier algorithm used in the framework. A feature combination method based on enhanced feature selection and classification is proposed according to the different features learned by each layer of the model. Combining block chain cryptography technology, distributed technology, consensus accounting mechanism of technology innovation, transaction data encapsulation into specific format data unit, encapsulated into a linear list in chronological order, using encryption algorithm trading transparency, traceability of data, security, credibility, and uniqueness in financial data analysis. The experimental results show that with the increase of the number of model layers, our method can significantly improve the classification accuracy. This result also verifies that the proposed model can learn more effective data representation features and also verifies the effectiveness of the proposed feature combination method.

Research on the risk of block chain technology in Internet finance supported by wireless network