With the widespread use of antibiotics, many bacteria have developed resistance. Antimicrobial peptides have broad applications in medicine because of their high antibacterial activity. In this paper, a neural network model is introduced to recognize and detect antimicrobial peptides. Our model consists of an embedded, convolutional, bidirectional LSTM, and full connection layers. The embedded layer is used to code different amino acid residues into different vectors. The convolutional layer and bidirectional LSTM extract peptide amino acid residue sequence information. The full connection layer maps the sequence information linearly to the interval from 0 to 1, as the peptide for the probability of antimicrobial peptides. Training and testing on several different datasets reveal that our model performs better than other proposed models.