The invention provides a tumor medical image classification method of zero sample evolution NAS based on double-index collaborative evaluation, which comprises the following steps: collecting tumor medical image data, and constructing a
data set containing a tumor focus image and a
normal tissue image; constructing an extended search space based on a
cell structure, wherein the search space comprises a basic operation set, the
cell structure and a network overall structure; steady-
state evolution algorithm parameters are set, and steady-
state evolution initialization is carried out to obtain an initial candidate architecture; taking a double-index comprehensive
score calculated by the information rate and the FIM stability as a comprehensive index for evaluating the current candidate architecture, updating the candidate architecture through a steady-state
evolutionary algorithm based on a dynamic optimization mechanism until the maximum evolutionary algebra is reached, and obtaining an optimal architecture; and training the optimal architecture by using the
data set, and carrying out tumor medical image classification by using the trained optimal architecture. According to the method, the dependence on the annotated data is reduced, the consumption of computing resources is reduced, and meanwhile, the diagnosis efficiency and accuracy are improved.