The fusion of hyperspectral and LiDAR data has broad application potential in the field of ground object classification. However, because of the Hughes phenomenon, extracting complete joint features from hyperspectral and LiDAR data is difficult. Therefore, this paper proposes a hyperspectral and LiDAR data fusion classification method based on an autoencoder to overcome the limitations in existing data fusion algorithms by eliminating heterogeneous data differences and achieving full fusion of cross-modal features. First, convolutional neural network models are used to extract deep features from hyperspectral and LiDAR data, effectively eliminating the differences in the original data space of heterogeneous data. Then, the feature information of different remote sensing data sources is fully learned by constructing a convolutional autoencoder for data feature fusion and adopting a cross-modal reconstruction strategy. Finally, the effectiveness of the proposed model is verified using the Houston and Trento datasets. Results show that the proposed method is remarkably superior to four methods, namely, SVM, ELM, EndNet, and MML, in terms of overall classification accuracy, average accuracy, and other evaluation indicators, thus confirming the superiority of the proposed method in urban land classification.