细菌拉曼光谱信号弱、相似度高且易被噪声干扰，使用传统机器学习方法对其分类时必须进行繁杂的光谱预处理，效率低下。为提高细菌拉曼光谱分类的准确率和效率，提出了一种基于密集连接的一维卷积神经网络模型Raman-net，无需额外的光谱预处理就能有效完成光谱分类。实验结果表明，Raman-net对Bacteria-ID公开数据集中30种细菌低信噪比拉曼光谱的分类准确率为84.26%，显著高于传统机器学习方法及对比方法。对于碳青霉烯类抗生素敏感和耐药的2种肺炎克雷伯菌表面增强拉曼光谱，Raman-net取得了99.16%的分类准确率。这表明对于细菌的普通拉曼光谱和表面增强拉曼光谱，Raman-net无需光谱预处理就能取得较好的分类效果，为致病菌的拉曼光谱鉴定提供了一种快速有效的方法。

Bacterial Raman spectrum is characterized by a weak signal, high similarity, and susceptibility to noise. Its classification using traditional machine learning approaches requires complex spectral preprocessing, and the efficiency is low. In this study, to enhance the accuracy and efficiency of bacterial Raman spectral classification, a one-dimensional convolutional neural network model Raman-net based on dense connection is suggested, which could efficiently complete spectral classification without additional spectral preprocessing. The experimental findings demonstrate that the classification accuracy of Raman-net for 30 bacterial low-signal-to-noise ratios Raman spectra in the Bacteria-ID public data set is 84.26%, which is substantially higher than that of traditional machine learning approaches and comparison approaches. Raman-net attained a classification accuracy of 99.16% for surface-enhanced Raman spectroscopy of 2 Klebsiella pneumoniae susceptible and resistant to carbapenems. This demonstrates that Raman-net can attain remarkable classification findings for ordinary Raman spectroscopy and surface-improved Raman spectroscopy of bacteria without spectral preprocessing, and offers a fast and efficient approach for Raman spectroscopy identification of pathogenic bacteria.