In order to solve the problems such as high time cost， inaccuracy and influence of parameter setting on algorithm performance when optimizing parameters of Convolutional Neural Network （CNN） by traditional manual methods， a variable Convolutional AutoEncoder （CAE） method based on Teaching-Learning-Based Optimization （TLBO） was proposed. In the algorithm， a variable-length individual encoding strategy was designed to quickly construct the CAE structure， and stack CAEs to a CNN. In addition， the excellent individual structure information was fully utilized to guide the algorithm to search the regions with more possibility， thereby improving the algorithm performance. Experimental results show that the classification accuracy of the proposed algorithm achieves 89.84% when solving medical image classification problems， which is higher than those of traditional CNN and similar neural networks. The proposed algorithm solves the medical image classification problems by optimizing the CAE structure and stacking CNN， and effectively improves the classification accuracy of medical image classification.