Intelligent vehicles need to use many characteristic parameters in unstructured road detection, which makes the feature fusion recognition difficult and computation complex, and the similarity of some road area and background may produce the mistake distinguishment and judgement of road identification. In order to solve the problems, an unstructured road detection method based on improved region growing with Principal Component Analysis-Support Vector Machine (PCA-SVM) rule was proposed. Firstly, the complex characteristic parameters such as color and texture of unstructured road were extracted, and then the PCA was used to reduce the dimension of the extracted characteristic information. The SVM trained with the primary characteristics reduced by PCA was used to be the classifier of the complex road cells. The priori knowledge such as the location of road, the initial cell and the characteristics of road boundary cells were used to improve the region growing method, and the classifier was used to decide the way of growing in cell growth for eliminating miscalculation area. The test results of actual roads show that, the proposed method has good adaptability and robustness, and can identify the unstructured road area effectively. The comparison results show that, compared with the traditional algorithm, the proposed method can shorten the calculation time by more than half through cutting characteristics from ten dimensions to three dimensions in ensuring the accuracy at the same time. The proposed method can also eliminate the 10% of miscalculation areas made by some similar areas of road and background for the traditional algorithm. The proposed method can provide a feasible way to shorten the recognition time and eliminate background interference in local path planning and navigation based on vision in the wild environment.