To address the issues of rating fluctuation and limited linear prediction ability in traditional collaborative filtering algorithms， a dual-channel recommendation algorithm based on Fast Fourier Transform （FFT） denoising and reverse-path collaborative enhancement named FRP-DCR （Frequency-domain denoising and Reverse-Path enhanced Dual-Channel Recommendation） was proposed， which integrates FFT-based denoising and reverse-path collaborative enhancement. First， user rating sequences were denoised in the frequency domain using FFT， so as to extract long-term preference trends， thereby reducing the impact of short-term emotional fluctuations on recommendation. Furthermore， a time decay mechanism was introduced to increase the algorithm's ability to represent temporal dynamics. Second， a reverse-path collaborative enhancement structure was designed during the construction of collaborative feature vectors， to simulate indirect user-item-user collaborative relationships， thereby addressing the inability of traditional methods to capture weak connections between users due to their reliance on direct user interactions. Finally， a regularized Broad Learning System （BLS） was applied to model the main rating structure， and a gradient boosting framework based efficient decision tree algorithm was adopted to model the nonlinear residuals， thereby improving prediction accuracy. The proposed algorithm， FRP-DCR， was compared with five baseline methods， including QP-DNN-TF-IDF （Quadratic Polynomial-Deep Neural Networks-Term Frequency-Inverse Document Frequency）， Light Graph Convolutional Network （LightGCN）， and Neural Collaborative Filtering （NCF）， to verify its performance. Experimental results on the MovieLens dataset show that FRP-DCR reduces the Mean Absolute Error （MAE） by 0.029 5 to 0.667 5， and reduces the Root Mean Square Error （RMSE） by 0.036 6 to 0.798 3. The algorithm achieves good performance in both metrics.