Flow temperature prediction is essential for assessing the health of river ecosystems. Water temperature data sets are often provided inconsistently in tasks that predict river water temperatures in different river basins, especially in different climatic regions. At the same time, spatial heterogeneity within different river basins significantly complicates water temperature prediction, which makes it challenging to establish a water temperature prediction model with strong generalization capabilities and stable prediction results. To solve this problem, the moving average encoding and DOY encoding of time series data into the time convolutional network model have been merged, thus constructing a time convolutional network model for time series data encoding (time-limited-TCN). The model effectively captured multimodal features of dynamic water temperature data from complex random time series, subsequently producing stable prediction results in different river basins. Thirteen hydrographic stations across four Bardeen rivers (Thames, Colorado, Mississippi and Sacramento) were used to test the proposed improved pre-temporal-TCN model and compare its performance with reference models (Air2Stream, Narx, Gru and Gboost). The results showed that the enhanced characteristics performed well in the river in the presence of human intervention, and that air temperature and DOY were important variables that influenced water temperature prediction. The proposed improved model shows that in cross-water water temperature prediction tasks, more stable and accurate prediction performance (average RMSE on the test set of at least 8.7 % better than the comparison model. Taking into account the characteristics and model performance, the proposed model should be a promising approach for the reconstruction of flow temperatures in several river basin data accumulation areas.