By means of significant test and co-linearity analysis, this paper proposes principal component linear encoding which selects the K-nearest neighbor visual word with the strongest linear correlation. The multiple linear regression method based on principal component is used to solve weak and instable coding caused by the visual words' co-linearity problem, improving the accuracy of the visual object classification effectively. Recognizing that the scarcity of the image quantify plays an important roles in the classification accuracy, the study analyzes the scarcity of the quantitative results obtained by the principal component linear encoding and then processes it with energy regularization to improve the classification efficiency further. The experimental results demonstrate that this method increases the recognition rate average over 1% than existing algorithms.