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The small size of the chickens and the shading of the chickens from each other are factors that make it difficult to identify the daily behaviour of laying hens. To address this problem, a method of daily behavior identification of laying hens based on SEEC-YOLO v5s was proposed. By adding a SEAM attention module (separated and enhancement attention module) to the output part of the YOLO v5s model and introducing an EVCBlock module (explicit visual center) to the feature fusion part, the perceptual field of the model was expanded, the recognition ability of the model for occluded targets was improved, and the recognition accuracy of the model for the six behaviors of standing, feeding, drinking, exploring, feather pecking and grooming of laying hens was improved. A statistical method was proposed to calculate the duration of daily behavior of laying hens based on the ratio of video frames to video frame rate, and various behavioral changes of laying hens at different times of the day and throughout the day were analyzed. The improved model was encapsulated and packaged to develop an intelligent identification and automatic statistics system for the daily behavior of laying hens. The test results showed that the mAP of SEEC-YOLO v5s model for six behaviors recognition was 84.65%, which was 2.34 percentage points higher than that of YOLO v5s model, and compared with that of Faster R-CNN, YOLO X-s, YOLO v4-tiny and YOLO v7-tiny models, the mAP was improved by 4.30 percentage points, 3.06 percentage points, 7.11 percentage points and 2.99 percentage points, respectively. The method can provide effective support for daily behavior monitoring and health condition analysis of laying hens, and provide a reference for smart farming.