Aiming at the problem of low accuracy in rainfall recognition based on rain sound signals and machine learning methods, this paper analyzes the frequency characteristics of rain sound signals, studies the static and dynamic features of Mel frequency cepstral coefficients of rain sound signals, and proposes a rainfall recognition method that combines Mel frequency cepstral coefficients (MFCC) with particle swarm optimization support vector machine (PSO-SVM). By extracting the static and dynamic features of MFCC from rain sound signals, using the importance evaluation mechanism built into the random forest algorithm for feature selection, and introducing PSO algorithm to fine tune the penalty parameter c and kernel function parameter g of SVM, the optimal parameter combination is found to achieve accurate rainfall identification. The experimental results show that MFCC features can more effectively characterize the characteristics of raindrop voiceprint signals compared to other features. After random forest feature selection, the overall accuracy of rainfall recognition increased by 5%. Combined with optimized PSO-SVM for rainfall recognition, the overall accuracy of rainfall recognition reached 91.1%. The accuracy of rainfall recognition for heavy and light rain also exceeded 90%, while the accuracy of rainfall recognition for moderate rain was slightly lower, but still reached 86.5%.