The invention provides a multifunctional self-
assembly peptide recognition method and
system, and relates to the technical field of biological
peptide recognition. The method comprises the following steps: firstly, obtaining
peptide sequences of a self-assembled peptide and a plurality of biological functional peptides, and inputting the peptide sequences into an embedded layer of a polypeptide function prediction model for 21-dimensional vectorization; and learning combination
modes and sequence features of amino acids through an
amino acid association
feature extraction layer of the LSTM network. Then, acquiring direct contact information among different amino acids on the basis of a fine-grained
feature learning layer of a multi-head self-attention mechanism; and finally, designing a Transform multi-task classifier which comprises five sub-task classifiers which are used for respectively predicting the self-
assembly function, the anti-
cancer function, the anti-inflammatory function, the antibacterial function and the
antioxidant function. According to the method, a multi-layer structure is adopted to carry out layer-by-layer
deep learning and optimization on polypeptide sequence features, efficient recognition and classification of multifunctional biological peptides are achieved, and finally the model performance is comprehensively evaluated through the accuracy rate, the precision rate, the
recall rate and the F1 value.