To address the limitations of existing methods that only focus on temporal or spatial features, this study proposes a novel approach for analyzing high-dimensional complex data using spatiotemporal features. The method, named Spatiotemporal Sparse Regularization Convolutional Autoencoder Unsupervised Domain-Adversarial Neural Network (SS-UDANN), employs a Sparse Regularization Convolutional Autoencoder (SRCAE) as an unsupervised feature extractor and incorporates an enhanced Domain-Adversarial Neural Network (DANN). A lightweight cross-dimensional Spatial Channel Attention (SCA) mechanism is integrated to extract spatiotemporal features efficiently while keeping computational costs low. Maximum Mean Discrepancy (MMD) is applied to regularize the feature extractor and domain discriminator, facilitating the extraction of domain-invariant features for effective data augmentation. Validation of SS-UDANN on the CICIDS2018 dataset for industrial network anomaly detection shows an accuracy of 98.66%. Additionally, the model achieves 95.39% accuracy when applied to data from a laboratory oil and gas full-process attack-defense testbed, further confirming the method"s effectiveness and demonstrating its potential for broader application in data processing.