To address the current problem of low accuracy in the recognition of feather pecking anomalies (including pecking and pecked) in laying hens, a method for feather pecking anomaly recognition was proposed based on an improved YOLO v6-tiny model. By introducing the DenseBlock structure into the YOLO v6-tiny model and incorporating the SPP module SPPCSPC into the CSP structure, the feature extraction capability of the YOLO v6-tiny model was enhanced, the sensory field of the model was expanded, and the detection accuracy of the model was improved. Based on the identification of feather pecking anomalies, how to classify individual laying hens was investigated based on the number of anomalies. The method to identify individual laying hens based on the YOLO v6-tiny model was proposed and identification results of feather pecking anomalies were input into the individual identification network to classify individual laying hens. At the same time, the change pattern of the number of anomalies at two different breeding densities and three different times of the day was also analyzed. The experimental results showed that the average precision (AP) of the optimized model were 92.86% and 92.93% for pecking and pecked anomalies, respectively, which were 1.61 percentage points and 1.08 percentage points higher than that of the YOLO v6-tiny model, 3.28 percentage points and 4.00 percentage points higher than that of the Faster R-CNN model, 6.15 percentage points and 6.63 percentage points higher than that of the YOLO v4-tiny model, 2.04 percentage points and 4.27 percentage points higher than that of the YOLO v5s model, and 5.39 percentage points and 3.92 percentage points higher than that of the YOLO v7-tiny model. The method can identify the abnormalities of pecking and pecked feathers, which provided technical support for the intelligent detection of abnormal behavior of laying hens. The results of classifying individual laying hens based on pecking abnormalities provided a basis for preferential breeding of individual laying hens.