Medium and long-term power load forecasting is a core link to ensure the stability and economy of power system planning and operation.Some studies convert the input data to the frequency domain through Fourier transform to obtain different signal components, thereby reducing the interference of noise. However, existing studies often indiscriminately handle all frequency-domain signals, causing the key frequency-domain components and irrelevant frequency-domain components to mix, which makes it difficult for the model to fully capture the features contained in the frequency-domain signals. Therefore, a multivariable long-term prediction model FTAformer that integrates frequency-domain analysis and attention mechanism is proposed. This model integrates time-domain and frequency-domain information and conducts collaborative modeling to enhance the model's ability to capture global features. Firstly, the input sequence is transformed into a frequency-domain signal by using the fast Fourier transform. A hierarchical filtering and isolation strategy is adopted to isolate the key frequency-domain components and suppress the noise. Then, the correlations among different variables are captured in the time domain through the multi-head attention mechanism, and the global representation of the sequence is modeled by using layer normalization and the feedforward network module. The experimental results show that on two public power load datasets, the predictive performance of this model is significantly higher than that of other benchmark models. Compared with the existing optimal model iTransformer, the mean square error and mean absolute error of the proposed method are reduced by 15.26% and 8.76% respectively in the multi-step prediction scenario, fully verifying the effectiveness and superiority of the collaborative modeling of frequency domain analysis and multi-head attention mechanism in medium and long-term power load forecasting.