Background: Different typing systems including capsular genotyping, lipopolysaccharide (LPS) genotyping, multilocus sequence typing (MLST), and virulence genotyping based on the detection of different virulence factor-encoding gene (VFG) profiles have been applied to characterize Pasteurella multocida strains from different host species. However, these methods require much time and effort in laboratories. Particularly, relying on one of these methods is difficult to address the biology of P. multocida from host species. Recently, we found that assigning P. multocida strains according to the combination of their capsular, LPS, and MLST genotypes (marked as capsular genotype: LPS genotype: MLST genotype) could help address the biological characteristics of P. multocida circulation in multiple hosts. However, it is still lack of a rapid, efficient, intelligent and cost-saving tool to diagnose P. multocida according to this system. Results: We have developed an intelligent genotyping and host tropism prediction tool PmGT for P. multocida strains according to their whole genome sequences by using machine learning and web 2.0 technologies. By using this tool, the capsular genotypes, LPS genotypes, and MLST genotypes as well as the main VFGs of P. multocida isolates in different host species were determined based on whole genome sequences. The results revealed a closer association between the genotypes and pasteurellosis rather than between genotypes and host species. Finally, we also used PmGT to predict the host species of P. multocida strains with the same capsular: lipopolysaccharide: MLST genotypes. Conclusions: With the advent of high-quality, inexpensive DNA sequencing, this platform represents a more efficient and cost-saving tool for P. multocida diagnosis in both epidemiological studies and clinical settings.