A schizophrenia classification and identification method, an operation control apparatus, and medical equipment. The method comprises: acquiring a data sample, and preprocessing the data sample to obtain a functional connection matrix (S100); inputting the functional connection matrix into a convolutional layer of a multi-kernel capsule network, providing several convolutional kernels of different sizes in the convolutional layer, and extracting connection information from the functional connection matrix by means of each convolutional kernel to obtain capsules, wherein the capsules are represented in the form of vectors, the directions of the vectors represent attributes, and the lengths of the vectors represent the probabilities of the corresponding attributes (S200); inputting the capsules into a capsule layer of the multi-kernel capsule network, iteratively updating a weighting matrix on the basis of a random capsule inactivation strategy and by using a routing protocol algorithm, and then converting the capsules into prediction vectors by means of the weighting matrix (S300); carrying out weighted summation on the prediction vectors and then inputting same into a classification capsule layer of the multi-kernel capsule network, and according to an edge loss function, updating a network connection weight value by using a backpropagation algorithm (S400); and performing training according to the multi-kernel capsule network to obtain a final weight value, and then performing calculation according to the final weight value to obtain a classification result of the data sample, so as to classify schizophrenia therefrom. The method can realize the classification and identification of schizophrenia, improve the classification accuracy, and improve the performance and robustness of a classification method.
SCHIZOPHRENIA CLASSIFICATION AND IDENTIFICATION METHOD, OPERATION CONTROL APPARATUS, AND MEDICAL EQUIPMENT