Due to a large amount of use and discharge of plastics, these plastics are broken into microplastics by the environmental effect and gather in the ocean in large quantities, leading to the accumulation of a large number of microplastics in the ocean, inrecent year. Microplastics are small in shape and difficult to identify their source and type. Laser Raman detection technology has been widely used in recent years which have fast, nondestructive and easy identification. In this paper, based on Raman spectral detection technology, an intelligent classification method combining wavelet processing and random forest algorithm is proposed to realize the rapid recognition of microplastics in seawater. The spectral data were collected by using laser Raman detection technology from six typical seawater microplastics standard samples(ABS, PA, PET, PP, PS, PVC), and the obtained spectra were pretreated by wavelet base DB7 and decomposition times 3 and standard deviation normalization. In order to improve the recognition speed, the spectral data is compressed at the same time. The data are respectively compressed to 64, 128, 256, 512 and 1 024 points, and their decision tree algorithm identification accuracy was 91.51%, 91.67%, 92.35%, 93.17% and 93.21% respectively. The random forest algorithm identification accuracy was 93.12%, 93.92%, 94.83%, 96.81% and 96.81%, respectively. The experimental results show that the Raman spectral compression of microplastics is the best compression point for efficiency and precision when the Raman spectral compression is 512 points, which can provide a reference for the Raman data compression of microplastics in practical engineering applications. Two recognition algorithms, decision tree and random forest, were used to study the Raman spectrum recognition of microplastics. The results show that the cross-validation accuracy of the random forest is higher than that of the decision tree. In order to further improve the identification accuracy, the model parameter optimization was carried out, and the cross-validation accuracy of the random forest method for identifying microplastics could reach 97.24% by using the optimized model parameters. It can provide a technical reference for the rapid identification of microplastics in seawater.