Abstract: Multilabel classification is a machine learning method to improve the performance of multi label joint decision by label correlation. In practical application scenarios, data labels are easy to be incomplete, which can lead to the reduction of available training data, and it is difficult to train the model adequately. Moreover, it is easy to cause the increase of label distribution variance, the deviation of correlation knowledge, and the limitation of multi label classification effect. To solve the problems, a weak supervised multi label classification method based on sample similarity was proposed. The method was arranged to use label correlation and sample similarity to recover labels to improve data utilization, and to embed label recovery into the training process to correct the bias in the model learning process. Based on the proximal accelerated gradient method, parameter optimization was carried out, and a multi label classification model was established for weak supervised learning scene. Experiments were completed with real data set. The results show that the method can effectively improve the classification ability of the model for the incomplete labels according to the similarity of samples, possessing high practical value.