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Currently, spatio-temporal fusion strategy is a key direction in traffic flow prediction. Current work employs a higher degree of spatio-temporal self-attention in order to capture spatio-temporal dependencies. However, the features to which this approach is applied are more targeted, increasing the computational complexity of the process and making it more difficult to capture long-range dependencies. This paper proposes a new framework for predicting traffic flow that enhances spatio-temporal features at multiple levels. The framework includes a periodic embedding module that captures temporal periodicity and encodes input data into more representative feature vectors for model training. Also, a component for fusing parallel channel attention has been designed to adaptively weigh the aggregation of features from global, local, and aggregated channels. This enhances the attention given to important feature information in the model. In addition, a multilevel sequential feature fusion enhancer has been designed that ensures feature processing at different levels. Experimental results on four public transportation datasets demonstrate that the innovative approach enhances the MAE metrics by an average of 2.50%, respectively, over all metrics in the baseline models. Notably, it reduces training time by approximately 50%. This paper also discusses ablation experiments to evaluate the performance of each module. [ABSTRACT FROM AUTHOR]