In order to carry out the research on the detection and analysis information of CNC machine tools,the machine tool in a healthy state was used to cut different workpiece materials,the data signal acquisition system was used to collect the mechanical characteristic information output in different cutting states,and the machine learning method was used to analyze and judge the characteristic information,an analysis and judgment model of workpiece material based on the fusion of machine tool spindle vibration signal and machine tool spindle load current characteristic information was proposed.Firstly,the spindle vibration signal and the load current signal of the machine tool were obtained,the algorithm of variational mode decomposition(VMD) was used to decompose the acquired signal under different processing condition，the multi-scale weighted permutation entropy(MWPE) of each acquired intrinsic mode function(IMF) was calculated for information fusion.Finally,the grey wolf optimization (GWO) algorithm was used to optimize the traditional support vector machine,and four common operating conditions were identified.The result shows that the method of combining feature extraction based on information fusion and GWO-SVM can accurately identify and judge the types of materials being processed by using the data feature information output of the machine tool processing state.