Perching behavior is an important habitual activity for laying hens. To achieve rapid and accurate identification of individual behaviors of hens in a free-range environment with perches, a three-dimensional free-range hen perching behavior recognition method that integrated machine learning with ultra-high frequency radio frequency identification technology was proposed. The data on signal strength indicator values from the collected leg band tags of hens were processed through Gaussian filtering and maximum value weighted filtering. Using the support vector machine algorithm, these signal strength indicator values were classified to transform individual location issues into a multi-region classification problem, thereby enabling the analysis of perching behavior. Experimental results showed that the accuracy of individual hen location reached 88.8%, with an average location error of 12.53cm. The duration of nocturnal perching for hens ranged from 9.80 h to 10.67h, with each hen requiring about 15cm of perch length. Behavior is one of the key parameters in animal welfare assessment. Hens exhibited a preference for perching at higher levels. Studies on individual perching behavior patterns of hens revealed a preference for the higher sides of the perch compared with the center of the upper level, and more so than the sides and center of the lower level, in descending order of preference as high sides, upper center, low sides and lower center.