In the field of time series data analysis, cross-domain data distribution shifts significantly weaken model generalization performance. To address this, an end-to-end time series domain adaptation framework, called TPN, is developed. This framework creatively integrates a temporal pattern activation module (TPAM) with a Transformer encoder. TPAM captures spatial and temporal dependencies of sequence features through dual-layer spatio-temporal convolution operations, combines Sigmoid and Tanh activation functions for the non-linear fusion of extracted features, and restores the original channel dimensions via linear projection, thus enhancing the model’s ability to extract temporal features. TPN also introduces an enhanced adversarial paradigm (EAP), which strengthens generator-discriminator-based collaborative adversarial learning through domain classification loss and operation order prediction loss. This effectively reduces data distribution discrepancies between source and target domains, improving the model’s domain adaptability. Empirical results on three public human activity recognition datasets (Opportunity, WISDM, and HHAR) demonstrate that TPN improves accuracy and F1 by up to 6% compared to existing methods, with fewer parameters and shorter runtime. In-depth ablation and visualization experiments further validate the effectiveness of TPAM and EAP, showing TPN’s strong performance in feature extraction and domain alignment.