Anticancer peptides (ACPs), which are able to specifically target and kill cancer cells, are promising cancer therapeutics. However, it requires significant time and cost to identify ACPs through biological experiments. To facilitate the ACP screening process, we propose the ACP prediction method CALM-AcPEP, a deep learning framework based on the ACmix module, Evolutionary Scale Modeling 2 (ESM2) and cross-attention. The ACmix module combines a convolution neural network and self-attention to recognize the original sequence representation, while the pre-trained ESM2 efficiently captures the evolutionary information of the peptide sequence. Then, the relationship between the original sequence and the evolutionary information is learned by the cross-attention mechanism, strengthening the representation of ACPs. The results of our study show that our proposed method is promising for the prediction of ACPs.