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Abstract
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.
Highlights
In recent years, the hot development of Bitcoin makes the underlying technology block chain gradually become the focus of institutions [1]
Block chain finance, which belongs to the concept of self-financing, has the characteristics of optimizing transaction mode, strengthening credit risk control, and reducing the cost of value transfer
4 Evaluation system of influencing factors In order to quantitatively evaluate the risk of block chain finance, a risk assessment system is constructed
Summary
The hot development of Bitcoin makes the underlying technology block chain gradually become the focus of institutions [1]. Because of the differences between the views of 25 experts, according to the majority principle and combined with the understanding of block chain finance, the data needed for fuzzy judgment matrix are obtained. Through the primary factor evaluation score matrix R and the primary index vector A2, the calculation shows that B = A2 *R, B = (0.803, 0.172, 0.025) At this point, we completed all block chain financial risk assessment procedures. The final result B of fuzzy analytic hierarchy process shows that the maximum value of 0.803 is the current evaluation level of the block chain financial risk, that is, the block chain financial risk is in a “high” situation.
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