Precise control of pitch variation in double bass playing has a decisive impact on musical expressiveness. Traditional analysis methods are difficult to accurately capture their complex patterns and require advanced algorithmic support. This study explores the application of support vector regression modeling in the analysis of pitch change patterns in double bass performance. By establishing a particle swarm optimized support vector regression (PSO-SVR) prediction model, we compare and analyze the pitch change characteristics under different bowing methods and propose a performance optimization method. The study firstly adopts the radial basis function as the kernel function, and uses the particle swarm algorithm to optimize the parameters of the SVR model, and obtains the optimal value of the penalty factor as 37.5431, and the optimal value of the kernel function kernel width parameter as 0.2876. The experimental results show that the PSO-SVR model has the mean squared error (MSE) of 0.0671 on the test samples, the coefficient of determination (R²) reaches 0.9415, and the average absolute error (MAE) is 0.2114, and the prediction performance is significantly better than the random forest model and the standard SVR model. Through the case study, the model was successfully applied to visualize and analyze the performance details of “The Morning of Miaoling”, revealing the mechanism of the influence of different playing techniques on the timbre effect. The results provide a quantitative basis for the optimization of double bass playing techniques, which has important theoretical value and practical significance.