In response to the problems that the existing Transformer-based Multivariate Long-Term Series Forecasting （MLTSF） models mainly extract features from the time domain， and it is difficult to find out reliable dependencies directly from the dispersed time points of the long-term series， a new decomposition and frequency domain feature extraction model was proposed. Firstly， a periodic term-trend term decomposition method based on the frequency domain was proposed， which reduced the time complexity of the decomposition process. Then， based on the extraction of trend features using periodic term-trend term decomposition， a Transformer network performing frequency domain feature extraction based on Gabor transform was utilized to capture periodic dependencies， which enhanced the stability and robustness of forecasting. Experimental results on five benchmark datasets show that compared with the current state-of-the-art methods， the proposed model has the Mean Squared Error （MSE） in MLTSF is reduced by an average of 7.6% with a maximum reduction of 18.9%， which demonstrates that the proposed model improves forecasting accuracy effectively.