Cell imaging and detection are of great significance in the field of biomedical research and clinical diagnosis, while label-free and high-throughput detections are particularly challenging. On the basis of dynamic scattering theory, this study built a dynamic scattering imaging system, proposed a spectral decomposition-based dynamic signal extraction algorithm, and achieved label-free and high-throughput cell classification by combining machine learning algorithms. Blood cells, EG7-OVA tumor cells and A549 lung cancer tumor cells are used to verify the current method. Experimental results show 98% accuracy for binary classification of blood cells and tumor cells, and 91% accuracy for the three-type classification of blood cells, EG7-OVA and A549. In summary, the proposed method provides high-throughput, label-free cell detection and classification, and is potential for clinical application.