In recent years, seawater pollution caused by microplastic waste has caught more and more attention. Raman spectroscopy, a non-destructive detection technique, has representative spectral characteristic peaks, making it easier to identify unknown samples. It has always been one of the popular detection methods in biochemical analysis. Deep learning has made remarkable achievements in feature extraction, target detection, and other fields. The feasibility of Raman spectroscopy based on the Markov transition field (MTF) combined with a convolution neural network (CNN)was explored for the accurate and efficient detection of microplastics. The Raman spectra of eleven types of microplastic samples were collected, and 100 spectra were collected for each sample; then, the spectral dataset was expanded through data augmentation. The one-dimensional Raman spectral data was converted into two-dimensional images using a Markov transition field to obtain a two-dimensional image spectral dataset. A small-sized multiple-kernel-based convolutional neural network (SSMKB-CNN) model with continuous small-scale convolutional kernels is designed, including one input layer, six convolutional layers, two pooling layers, one flattened layer, two fully-connected layers, and one output layer. The classification performance of the model is compared with the classification results of AlexNet, VGG16, and ResNet50 deep convolution neural network models based on a two-dimensional MTF image spectral dataset, along withthree classical machine learning classifiers based on a one-dimensional spectral dataset, including K-nearest neighbors (KNN), random forest (RF) and support vector machine (SVM)with three kernel functions (rbf, Linear and Poly). It could be seen from the training curves and the classification confusion matrix that the loss and accuracy curves of the four CNN models are stable and can achieve a good training effect. The accuracy rate of the proposed SSMKB-CNN model reaches 97.04%, and the macroprecision rate, recall rate, and F1-score are 97.05%, 95.06%, and 97.02%, respectively, which is superior to the other three CNN models used for comparison and the three machine learning classifiers. Each training epochconsumes 9 seconds, less than the three CNN models. Overall, the proposed SSMKB-CNN model has the best classification performance. The experimental results show that the Raman spectrum and SSMKB-CNN model combined with MTF can accurately and efficiently extract spectral features and make precise predictions, and the qualitative identification of microplastic samples using the Raman spectrum is realized. It can provide a method reference for the recognition technology of actual microplastic contaminants in seawater.