Time series data pose unique challenges for forecasting due to their complex long- and short-term patterns and multi-period characteristics. Traditional fixed-scale patching methods are difficult to effectively capture multi-period information, while periodicity and trend changes further increase the modeling difficulty, affecting forecast accuracy and interpretability. Based on the above problems, this paper proposes a multi-periodic model MDTDNet based on dual time-dependent learning. The model firstly acquires multi-period information adaptively by Fourier transform; then for each period, combined with the seasonal trend enhancement module, it improves the semantic expression of the subsequence through period patch design, frequency domain seasonal enhancement and time domain trend enhancement. A dual time-dependence module is introduced to realize feature extraction and fusion by capturing different time-dependent patterns inter- and intra-patchs by means of a long-term change extractor and a local fluctuation extractor, respectively. The experimental results show that the experimental results of the models in all six datasets outperform the current optimal model, PatchTST, with an average decrease of 9% in the mean square error (MSE) on ETTh1 dataset, with a maximum decrease of 10.14%.