Small servo motors play a critical role in fields such as aerospace, robotics, and automation systems, where their performance directly impacts the stability and precision of the system. Traditional manual detection methods suffer from low efficiency, high error rates, and other issues, making them unsuitable for modern production requirements.Support vector machine (SVM) algorithms have been widely utilized in the remote sensing community due to their high performance with small training datasets [1]. This paper proposes an intelligent detection device for small servo motors based on Support Vector Machine (SVM), combining data acquisition, feature extraction, and intelligent classification algorithms to provides precise classification and prediction based on these extracted patterns [2]. The device uses an automatic loading mechanism to enable multi-channel synchronous testing and employs SVM algorithms to identify fault modes and comprehensively evaluate the performance of servos by analyzing parameters such as torque, current, and voltage. Experimental results show that this method outperforms traditional methods in terms of detection accuracy and efficiency, providing an effective solution for standardizing the detection of small servo motors.