By studying on pulse coupled neural network (PCNN) and Raman spectra qualitative analysis, a method based on PCNN for Raman spectra qualitative analysis was proposed. After encoding the Raman spectra by using PCNN neurons’ characteristics of fatigue and refractory period, the improved Horspool algorithm was used to match the code corresponding to the detected sample with all of the base code in the database one by one, and then their matching similarity was acquired to determine the sample type. Experimental results and analysis of data proved that the method proposed in this paper is accurate and effective for Raman spectra qualitative analysis. Meanwhile, traditional qualitative analysis method based on spectral template has some deficiencies, like that it is difficult to determine the characteristic peak of the detected sample and the matching analysis process has a high degree of redundancy. While our proposed method not only can avoid these deficiencies very well, but also needs a small amount of data storage. The requirement of the storage space was only 5.8% of that used in the traditional qualitative analysis method based on spectral template.