Optimization of the Economic and Trade Management Legal Model Based on the Support Vector Machine Algorithm and Logistic Regression Algorithm

Abstract

Nowadays, various algorithms are widely used in the field of economy and trade, and economic and trade management laws also need to introduce scientific and effective data models for optimization. In this paper, support vector machine algorithm and logistic regression algorithm are used to analyze and process the actual economic and trade case data and bank loan user data, and a hybrid model of support vector machine and logistic regression is established. This study first introduces the basic definitions and contents of the support vector machine algorithm and logistic regression algorithm, and then constructs a hybrid model by randomly dividing the data, first using the support vector machine algorithm to calculate the results, and then inputting them into the logistic regression algorithm. The first mock exam is that the efficiency of the hybrid model is much higher than that of the single model. This study mainly optimizes and upgrades the legal system of economic and trade management from two aspects. In the prediction of economic and trade legal cases, the hybrid model is significantly better than FastText and LSTM models in accuracy and macro recall performance. In terms of credit risk prediction of economic and trade loan users, the subset most likely to default in the loan customer set is obtained.