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A complete blood cell count is an important testing technique to evaluate overall health condition in medical diagnosis.In order to solve the problem that traditional blood cell counters and other devices are cumbersome and time-consuming for the artificial counting procedure of blood cells, a blood cell recognition algorithm based on convolutional neural networks is proposed, that is, three types of blood cells are automatically identified and counted based on Res2Net and YOLO object detection algorithm.The performance of the blood cell identification model is enhanced by incorporating Res2Net into the YOLO model to extract multiscale features represented by fine-grained and increase the range of receptive field in each network layer.After training and testing on an public blood smear image dataset, it can automatically identify and count red blood cells, white blood cells, and platelets, and the accuracy of identification reaches 93.44%, 96.09%, and 96.36%, respectively.Compared with other recognition models based on convolutional neural networks, the efficiency of blood detection can be significantly improved due to the high re-cognition accuracy and strong generalization.