拉曼光谱物质定性识别已被广泛的应用于化工、安防、缉毒等行业和研究领域，但是传统的拉曼光谱分析技术依赖于光谱数据库，通过光谱特征提取进行识别。特征提取是拉曼识别的关键处理步骤，通常利用主成分分析，因子分析等方法进行特征提取，而后通过KNN，SVM和随机森林等方法进行光谱特征定性识别，当拉曼数据库不存在待定性物质时，易造成待检测物质的错误分类。针对此问题，提出一种基于卷积神经网络的对数据库缺少物质光谱识别方法。在实验过程中，采用九类，200余种精神类药品拉曼光谱作为测试对象，通过搭建卷积神经网络自动特征提取并利用Softmax分类器将200余种物质，按照Amphetamine，cathinone，cannabinoids等九种类别进行定性分析。通过与传统机器学习方法如K近邻，支持向量机等方法进行比较，基于卷积神经网络的模型识别准确性有显著提高，该方法可为拉曼光谱数据库的光谱识别检索提供一种新的识别方法。

Raman spectroscopy has been widely used in chemical industry, security,anti drug and other industries and research fields, but the traditional Raman spectroscopy analysis technology relies on the spectral database, through the spectral feature extraction for identification. Feature extraction is the key step of Raman recognition. Principal component analysis, factor analysis and other methods are usually used for feature extraction, and then KNN, SVM and random forest methods are used for qualitative identification of spectral features. When there is no undetermined substance in Raman database, it is easy to cause the wrong classification of the substance to be detected. In order to solve this problem, a method based on convolution neural network is proposed to identify the lack of substance spectrum in database. In the process of the experiment, we use nine categories, more than 200 kinds of psychotropic drugs Raman spectrum as the test object, through the construction of convolution neural network automatic feature extraction, and use softmax classifier to analyze more than 200 kinds of substances according to nine categories, such as amphetamine, cathinone, cannabinoids and so on. Compared with the traditional machine learning methods such as  k nearest  neighbor and support vector machine, the accuracy of model recognition based on convolution neural network is significantly improved. This method can provide a new recognition method for Raman spectrum database.