PAthreader is a new method for the remote homologous template recognition and protein folding pathway prediction. First, the structure profile is extracted from PAcluster80, a template library constructed by clustering PDB and AlphaFold DB with a threshold of 80% structural similarity. Meanwhile, the distance profile is predicted by our in-house DeepMDisPre. Then, a three-track alignment algorithm is proposed to align the query sequence to each cluster seed to get the maximum alignment score (alignScore). As an supplement to alignScore, the physical and geometric features of the alignment structure are extracted and fed into a convolutional network with self-attention to predict DMScore (pDMScore), a global structure scoring metric linearly weighted with the alignScore for the template ranking. Subsequently, the frequency distribution of the residue is calculated based on different distance deviation thresholds of the templates. Finally, the secondary structure and the frequency distribution are employed to identify folding intermediates for exploring the folding pathway.