To address the issue of local dependency loss during long-term forecasting due to increased sampling interval after time series segmentation， a time series forecasting model based on Segmented Attention Mechanism （SAMformer） was proposed. Firstly， time static covariates were fused with original data in proportion explicitly to enhance time domain information representation ability of the data. Secondly， two continuous linear layers with bias and an activation function were introduced to fine-tune the fused data， thereby improving the model’s ability to fit nonlinear data. Thirdly， a dot product attention mechanism was introduced in each segment of the segmented series to capture local feature dependencies. Finally， a cross-scale dependency based encoder-decoder architecture was utilized to predict time series data. Several experiments of the proposed model were carried out on five public time series datasets， and the results show that compared with other supervised learning based time series forecasting models， Crossformer， Pyraformer， and Informer， SAMformer reduces the Mean Squared Error （MSE） and Mean Absolute Error （MAE） by 2.0%-62.0% and 0.9%-49.8% respectively. Besides， through ablation experiments， the completeness and effectiveness of the proposed different components are verified， which further shows that fusion of time domain information and intra-segment attention mechanism is helpful to improve the accuracy of time series forecasting.