Abstract
To address the shortcomings of standard convolutional neural networks (CNNs), the model structure is complex, the training period is lengthy, and the data processing technique is single. A modified capsule network is presented to optimize hierarchical convolution-the algorithm for identifying mental health conditions. To begin, two types of data processing are performed on the original vibration data: wavelet noise reduction and wavelet packet noise reduction; this retains more valuable information for mental health identification in the original signal; secondly, the CNN employs the concept of hierarchical convolution, and three distinct scaled convolution kernels are utilized to extract features from numerous angles; ultimately, the convolution kernel's extracted features are fed into the pruning strategy's capsule network for mental health diagnosis. The enhanced capsule network has the potential to significantly speed up mental health identification while maintaining accuracy. It is time to address the issue of the CNN structure being too complex and the recognition impact being inadequate. The experimental findings indicate that the suggested algorithm achieves a high level of recognition accuracy while consuming a small amount of time.