In recent years, many self-attention models have achieved good sequence recommendation performance by, capture the sequential dependencies between users and items. However, user behavior data inevitably contains noise, and the embedding of location information may interfere with item embedding semantics, causing noise in the data to further increase. At the same time, these self-attention models ignore the impact of high-relevance user-item interactions on the next item. To address these problems, we propose a new sequential recommendation system (AMFRec). Specifically, we adopted a three-way information (sequence, cross-channel, cross-feature) adaptive fusion scheme enhanced by a filtering algorithm. The proposed system is completely based on the MLP architecture attenuates noise in the frequency domain to reduce its impact on the model, and is naturally sensitive to location information. Finally, we designed a squeeze incentive module suitable for recommendation systems to activate multiple highly relevant projects. Experiments were conducted on three widely used datasets to demonstrate the effectiveness and efficiency of the proposed method.