The lag of the law makes it difficult to keep up with the pace of technological iteration, and improving intelligent technology to optimize financial regulation is in line with the hot topics of the times. Propose an improved intelligent supervision model for the securities market by combining financial time series modeling, SSA optimization algorithm, and adversarial training techniques. Select sample data such as risk indicators of listed banks, use bivariate regression empirical research method to improve the accuracy of securities market volatility risk warning and regulation, and solve the problem of legal and technical mismatch in securities market regulation. Due to the lack of coordination between technology and law in a complex market environment, the model’s adaptability needs further improvement. Future research will further optimize intelligent regulatory models in order to provide more efficient market risk regulation methods, innovative decision-making management for the financial industry, and promote the deep integration of intelligent technology and financial law.