The rapid, non-destructive, and quasi-deterministic analysis for drugs is a critical issue in the field of drug prevention and control. The experiment sorted the spectral and chromatographic data from 159 cannabis oil samples representing four types. We establish classification models for different data sets using the supervised pattern recognition methods of Fisher discriminant analysis and K-nearest neighbor analysis, and then compare the effects of single and fusion models on the analysis results. According to the results, in the process of identification and classification of four types of cannabis oil, the fusion model based on spectral and chromatographic data sets has a higher classification effect than other data sets, and the fusion model based on K-nearest neighbor analysis has achieved the best classification effect, with an overall classification accuracy of 1. This research enables the rapid and accurate qualitative analysis of different types of cannabis oil, as well as providing evidence and clues for accurately identifying the source of seized drugs and trying upstream and downstream drug crime cases related to facts.