Aiming at the problems of existing domain adaptation algorithms based on adversarial learning that they cannot effectively learn transferable features and have poor generalization ability, a domain adaptation algorithm based on feature and category alignment(FCDA) is proposed in this paper. First of all, in view of the shortcomings of the maximum mean discrepancy(MMD) measurement criteria, a new improved maximizes the intra-domain density(MID) measurement function is obtained, which measures the distribution divergence between the source domain sample features with the same label, and the distribution divergence between the target domain sample features with the same label, so as to maximize the class density of similar samples in the domain, thereby the class error rate is reduced. Then, in order to learn the abstract and transferable features of the target sample at a deeper level, and reduce the difference between domains, a residual correction block is added after the feature extraction network to deepen the basic network, and the transferability of its features is improved. Finally, the acquired features are passed through the joint discriminant network, and the alignments at the class-level and the domain-level are achieved with the adversarial loss function. The proposed algorithm has an average accuracy of 88.6% for the dataset Office-31 and an average accuracy of 67.7% for the dataset Office-Home. Compared with other algorithms, the proposed algorithm has better generalization ability and higher classification performance.