To solve the problem of the long tail effect failing to fully consider users' personalized behaviors when dividing popular items and long-tail items， a recommendation method integrating user behaviors and improved long-tail algorithm was proposed. Firstly， Bidirectional Encoder Representations from Transformers （BERT） was utilized to encode item attribute information， and items were clustered according to the encoding results. At the same time， personalized popular items and long-tail items were divided again for the user according to the user's interaction records with different clusters， thereby integrating personalized user behaviors into the process of dividing popular items. Secondly， the user's popularity sensitivity was evaluated on the basis of interaction records， thereby fully considering the extent of popularity factors influencing the user. Finally， a novel negative sampling method was proposed， in which different negative sampling strategies were adopted for users with varying popularity sensitivities， and user preference clustering was integrated to select higher-quality negative samples. Experimental results on three public real-world datasets demonstrate that compared to the traditional 80-20 division method， the proposed personalized division method is improved in terms of recall， Hit Rate （HR）， and Normalized Discounted Cumulative Gain （NDCG）. In the resampling experiment， the average NDCG@20 for the original， popular， and long-tail data across the three datasets increased by 0.45， 1.03， and 2.33 percentage points， respectively. When compared with the optimal baseline model NNS （Noise-free Negative Sampling）， improvements in metrics such as HR and NDCG were demonstrated by the proposed negative sampling method. Improvements of 2.72， 1.37， and 5.93 percentage points in the average NDCG@20 metrics were achieved on the raw data， popular data， and long-tail data， respectively， which validated the effectiveness of the proposed negative sampling method.