Signing-pen inkblok is an important evidence in the forgery cases which comprise documents，certificates，checks etc．By analyzing the ink types of suspicious handwriting，forensic science experts could recognize suspect's writing behaviors and infer whether the writing utensils are identical．In this paper，a new method for the rapid and nondestructive identification on types of black signing-pen ink by combining hyperspectral imaging（HSI） technique and machine learning was proposed．Primarily，thirty-six collected black signing-pens were numbered in turn through different brands and models，then each of them were used to write their own number three times repeatedly on the same specification of white A4 printing paper as the handwriting for the test.After that，the hyperspectral imager was used to collect the hyperspectral images of the prepared handwritings，and black and white calibration was performed for all the images in order to reduce the effects of dark current of camera and changes of light intensity on the image signal．And then，ENVI was used to read the hyperspectral image information，and eighteen representative region of interest（ROI） were selected manually on the hyperspectral image of each pen's handwriting.In term of the average spectra calculation of the extracted region of interest，a total of 648 average spectra were finally obtained as the sample set．For investigating the effect of different preprocessing methods，Savitzky－Golay smoothing，Z－Score standardization and their combined spectral pre-processing methods were respectively used to preprocess the handwriting original spectra data of 450－950 nm. Furthermore，linear discriminant analysis（LDA） and random subspace method－linear discriminant analysis（RSM－LDA） were respectively adopted to establish two identification models of black signature-pen ink types，and comparing their merits and drawbacks．The experimental results showed that：（1） The hyperspectral images of black signing-pen ink were too consistent to identify，so it was necessary to process by machine learning algorithm；（2） Different pre-processing methods had little influence on the identification accuracy of RSM－LDA model，while LDA model had better identification accuracy after it was combined with spectral pre-processing method；（3） The average classification accuracy rates of LDA model for training set，cross validation set and testing set were 99.54%，98.16% and 84.50% respectively；（4） Compared with LDA model，RSM－LDA model had better classification effect. The average classification accuracy rates for training set，cross validation set and testing set could reach 100%，99.09% and 90.70%，respectively.And the accuracy rates，precision rates and recall rates of each type of samples were all higher than those of LDA model．The AUC value of RSM－LDA model was 0.998 3，which indicated that the RSM－LDA model's performance was remarkably good．To sum up，RSM－LDA model was performed better than LDA model in processing redundant data，possessing anti-interfered，solving over fitting problem，getting better classification accuracy etc.，which exhibited better classification effect and robustness.Therefore，hyperspectral imaging technique combined with RSM－LDA model could be used to achieve the rapid and nondestructive classification and discrimination of different brands and models of black signing-pen ink.