A computer-implemented method for predicting structure information of a protein, characterized in that the method comprises: performing sequence alignment query in a first database according to an amino acid sequence of the protein to obtain multi-sequence alignment data; performing feature extraction on the multi-sequence alignment data to obtain an initial sequence feature; processing the initial sequence feature by using a sequence feature augmentation model to obtain an augmented sequence feature of the protein, the initial sequence feature and the augmented sequence feature being position-specific scoring matrices, PSSMs, the sequence feature augmentation model being a machine learning model trained by using an initial sequence feature sample and an augmented sequence feature sample, the initial sequence feature sample being obtained by performing sequence alignment query in the first database according to an amino acid sequence sample, the augmented sequence feature sample being obtained by performing sequence alignment query in a second database according to the amino acid sequence sample, and a data scale of the second database being greater than a data scale of the first database, wherein a greater data scale includes a greater database storage; and predicting structure information of the protein based on the augmented sequence feature, wherein the predicting structure information of the protein based on the augmented sequence feature comprises: predicting the augmented sequence feature by using a protein structure information prediction model to obtain the structure information of the protein, wherein the protein structure information prediction model is a model trained according to a sequence feature of a protein sample and structure information of the protein sample. performing sequence alignment query in a first database according to an amino acid sequence of the protein to obtain multi-sequence alignment data; performing feature extraction on the multi-sequence alignment data to obtain an initial sequence feature; processing the initial sequence feature by using a sequence feature augmentation model to obtain an augmented sequence feature of the protein, the initial sequence feature and the augmented sequence feature being position-specific scoring matrices, PSSMs, the sequence feature augmentation model being a machine learning model trained by using an initial sequence feature sample and an augmented sequence feature sample, the initial sequence feature sample being obtained by performing sequence alignment query in the first database according to an amino acid sequence sample, the augmented sequence feature sample being obtained by performing sequence alignment query in a second database according to the amino acid sequence sample, and a data scale of the second database being greater than a data scale of the first database, wherein a greater data scale includes a greater database storage; and predicting structure information of the protein based on the augmented sequence feature, wherein the predicting structure information of the protein based on the augmented sequence feature comprises: predicting the augmented sequence feature by using a protein structure information prediction model to obtain the structure information of the protein, wherein the protein structure information prediction model is a model trained according to a sequence feature of a protein sample and structure information of the protein sample. predicting the augmented sequence feature by using a protein structure information prediction model to obtain the structure information of the protein, wherein the protein structure information prediction model is a model trained according to a sequence feature of a protein sample and structure information of the protein sample.