In order to effectively manage financial risks, the application of data analysis and data mining technology in financial investment risk management has been proposed. Taking the daily closing price data of Shanghai Stock Exchange Index and Shenzhen Stock Exchange Index as the research object, the marginal distribution is obtained by using the nonparametric kernel distribution estimation function method, the parameters of the commonly used Copula function are estimated by Matlab software, and the Euclidean distance is used as the evaluation index of the Copula model. Then, based on binary normal Copula and t-Copula, we use the new method proposed in this paper to construct a new Copula function. Through comparative analysis, we find that the constructed Copula function can better fit financial data than the commonly used Copula function. Finally, Monte Carlo method is used to calculate the value at risk of the portfolio under different weight values. The results show that when the investment weights are different, the VaR values obtained differ greatly. It can be further found that when the investment weight of the Shanghai Stock Exchange Index is 0.6 and the investment weight of the Shenzhen Stock Exchange Index is 0.4, the VaR value obtained by using these four Copula functions is the smallest, so it can be concluded that the risk of investors selecting this portfolio is the smallest.