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The crying of newborns can to some extent reflect their health status. Healthy newborns may cry loudly, while unhealthy newborns may cry weakly and weakly. However, as a typical non-stationary signal, the complexity and diversity of sound signals place higher demands on signal processing techniques. Therefore, a sound recognition model based on improved particle swarm optimization algorithm was proposed to analyze the health status of newborns through sound. The model preprocesses the sound data through wavelet denoising, and then combines the improved particle swarm optimization algorithm with backpropagation algorithm to optimize the sound recognition model. The experimental results show that when the dataset is 1000, the signal-to-noise ratio of the wavelet denoising model is 0.97, the structural information loss rate is 0.18, and the intersection to union ratio is 0.96. The accuracy of the improved particle swarm optimization algorithm model for different types of crying sounds is 0.87, 0.83, 0.97, and 0.88, with RMSE values of 0.09, 0.07, 0.05, and 0.07, respectively. The experimental results show that the proposed voice recognition and health detection model can effectively improve the recognition accuracy and detection efficiency of sound data, and evaluate the health status of newborns.