To establish a rapid and precise identification and classification model for material evidence on note paper,44 note paper samples underwent Laser Confocal Micro-Raman (LCM-Raman) analysis.By integrating Principal Component Analysis (PCA),Hier- archical Cluster Analysis (HCA),and Linear Discriminant Analysis (LDA),the samples were identified and classified.Firstly,ac- cording to the different Raman spectrum characteristic peaks of inorganic fillers in the samples,the samples were Qualitatively analyzed and preliminarily divided into19categories.Secondly,for the sake of a more scientific and accurate classification,The principal compo- nent analysis was used to reduce the dimension of the Raman spectrum data of the samples,and the system clustering and linear dis- criminant analysis were used to determine that the best classification of the samples was 4 categories.The samples from the same place of origin had good aggregation,and the classification accuracy and cross validation classification accuracy were both 100%.Finally,The PCA-HCA-LDA classification model was used to predict the ungrouped sample of note paper.As concluded from the results,LCM-Ra- man combined with PCA-HCA-LDA classification model can distinguish note paper samples The generalization accuracy of the model is 100%,and the classification results are scientific and accurate.