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The histopathological image diagnosis of breast cancer using machine learning saves a lot of manpower and material resources, so it is of good practical significance to improve the accuracy of histopathological image recognition of breast cancer. Aiming at the problem that the observation domain of single classifier and ensemble learning classifier model is limited and easy to fall into local optimality, a classifier model based on joint training is proposed. Through the mutual influence of a single classifier, the observation perception domain is expanded to find the estimated point with the least loss, and the hyperparameters are iteratively optimized according to the estimated point and then jointly trained to jointly train the classifier with the best fitting performance, which not only absorbs the desirability of different classifier models to enhance the generalization ability, but also increases the model observation domain to obtain the global optimal faster while improving the recognition accuracy. Experiments show that the proposed jointly trained classifier can improve the classification performance of breast cancer histopathological images,and the accuracy of benign and malignant classification of images at different magnifications of 40×, 100×, 200× and 400× is 99.67%, 98.08%, 99.01% and 96.34%, respectively.