The invention discloses a robot barrier identification method based on a gradient histogram and a support vector machine. The method comprises two parts of a characteristic extraction stage and a target identification stage, for the characteristic extraction stage, a characteristic extraction algorithm of a power transmission line barrier of a principal component gradient histogram is proposed, the characteristic that typical barriers have different structures and space layouts is utilized, statistics characteristics of common online barriers are calculated, characteristic extraction is carried out by utilizing an HOG algorithm, characteristic points irrelevant to illumination and scale change can be acquired, interference can be effectively removed, moreover, dimension reduction operation for acquired characteristic vectors can be realized by utilizing main component analysis to acquire the principal component gradient histogram, irrelevant characteristics can be effectively reduced, operand is reduced, least characteristics are utilized to establish a characteristic set of the corresponding barriers, and excellent support is provided for next target identification; for the target identification stage, the linearity support vector machine is utilized for identification, and the excellent identification effect is acquired.