Aiming at the existing multivariate long time series prediction model in the medium and long term prediction of photovoltaic (PV) power, which has the problem of poor prediction results due to insufficient feature extraction, a multivariate long time series prediction model FFTEMixer based on learning in both frequency and time domains is proposed, which is capable of accurately predicting the PV power while maintaining a high operational efficiency. The model first uses the fast Fourier transform to project time-series data into the frequency domain. It then selectively enhances or suppresses specific frequency components through learnable frequency filters to extract global features and inter-variable correlation features. Next, an interactive convolution module is used to learn local dependencies, further enhancing feature expression capabilities. Subsequently, a feature fusion module is employed to further integrate periodic features, and establishes associations between feature variables and time stamp covariates. Finally, a multi-head self-attention mechanism is employed to comprehensively model the long-term dependencies and temporal dependencies of the sequence, thereby achieving comprehensive feature extraction from time-series data. Experimental results show that on two publicly available photovoltaic power generation datasets, the model′s predictive performance significantly outperforms the baseline model, with mean squared error (MSE) and mean absolute error (MAE) consistently achieving the lowest values. Compared to the current mainstream second-best model, its MSE and MAE are reduced by 12.6% and 15.8%, respectively, validating the model′s effectiveness.