Abstract: As a weakly supervised learning framework, the goal of partial label learning is to learn a multi classification model from the partial label data with noisy labels. In order to solve the problem of insufficient utilization of label information and poor classification effect in partial label learning, a partial label learning algorithm based on label-aware disambiguation was proposed. The algorithm determines the similarity relationship between instances through the discrimination information of collaborative feature space and label space, and uses the similarity relationship and the reconstruction error in label space to implement the disambiguation process. In the process of training classification model, a framework was proposed based on the least square loss, which can simultaneously train the prediction model and eliminate the labeling ambiguity. And the optimal classification model is obtained by alternating iterative optimization method. Experiments on 3 artificial UCI data sets and 6 real partial label data sets show that PL-LAD algorithm has better classification performance compared with existing algorithms.