A method of automatically measuring the phenotype and predicting the body weight of Pareuchiloglanis anteanalis based on the keypoint detection was developed to improve the efficiency of obtaining the phenotypic data of P. anteanalis and reduce errors caused by manual measurements. 951 images were collected from the ventral，lateral，and dorsal views of P. anteanalis.Keypoints were annotated in the COCO format.Traditional body size including body length，body height，and interorbital distance and indexes of frame distance were calculated based on the coordinates of keypoints.RTMPOSE，LiteHRNet，YOLOv12n-pose，and YOLOv8n-pose were comparatively evaluated under the same configuration of training，and YOLOv8n-pose was determined to be used for the automatic measurement of phenotypic parameters.Results showed that YOLOv8n-pose had a precision of 94.70% on the test set，with mean relative error （MRE） of 5.45% for phenotypic measurements，and the relative error of most phenotypes was controlled within 10%.Key indexes of phenotype including the distance between pectoral-fin origins （X₅），the distance from the pectoral-fin base to the right pelvic-fin base （X₇），body height （X₁₀），head length （X₁₂），and interorbital distance （X₁₄），were further selected by combining correlation and collinearity analysis to establish a multiple regression model for predicting body-weight，yielding an R2 of 0.97 on the test set.It is indicated that the proposed method can achieve automated measurement of phenotype and quantitative estimation of body weight of P. anteanalis.It will provide data support for monitoring growth and evaluating selective breeding of small sisorid freshwater fishes.