The current public datasets of Pedestrian Attribute Recognition （PAR） have the characteristics of complicated attribute annotations and various collection scenarios， leading to the large variations of the pedestrian attributes in different datasets， so that it is hard to directly utilize the existing labeled information in the public datasets for PAR in practice. To address this issue， a multi-source and multi-label PAR method based on domain adaptation was proposed. Firstly， to transfer the styles of the different datasets into a unified one， the features of the samples were aligned by the domain adaption method. Then， a multi-attribute one-hot coding and weighting algorithm was proposed to align the labels with the common attribute in multiple datasets. Finally， the multi-label semi-supervised loss function was combined to perform joint training across datasets to improve the attribute recognition accuracy. The proposed feature alignment and label alignment algorithms were able to effectively solve the heterogeneity problem of attributes in multiple PAR datasets. Experimental results after aligning three pedestrian attribute datasets PETA， RAPv1 and RAPv2 with PA-100K dataset show that the proposed method improves the average accuracy by 1.22 percentage points， 1.62 percentage points and 1.53 percentage points respectively， compared to the method StrongBaseline， demonstrating that this method has a strong advantage in cross dataset PAR.