Heterogeneous domain adaptation is a more challenging problem than homogeneous domain adaptation. The transfer effect is not ideally caused by shallow structure which cannot adequately describe the probability distribution and obtain more effective features. In this paper, we propose a heterogeneous domain adaptation network based on autoencoder, in which two sets of autoencoder networks are used to project the source-domain and target-domain data to a shared feature space to obtain more abstractive feature representations. In the last feature and classification layer, the marginal and conditional distributions can be matched by empirical maximum mean discrepancy metric to reduce distribution difference. To preserve the consistency of geometric structure and label information, a manifold alignment term based on labels is introduced. The classification performance can be improved further by making full use of label information of both domains. The experimental results of 16 cross-domain transfer tasks verify that HDANA outperforms several state-of-the-art methods.