In order to recognize potato typical insect pests accurately and quickly, a new feature extraction and recognition method based on wavelet and space domain was proposed. The processing object in the method was the segmented image of insect pests separated from complex background by the twodimensional Otsu method and morphological method. Aiming at the processing object, totally 12 invariant texture features of high frequency covariance matrix eigenvalues and low frequency lower order moments (HELM) were extracted from the high frequency images in the horizontal, vertical and diagonal directions, forming a Gaussian space model, and from low frequency image decomposed by sym8 wavelet function. Meanwhile, 4 Hu moments with invariant shape features were extracted from the binary image of the processing object. As thus, 16 pest features were put into support vector machine (SVM), and the results of insect pest classification could be obtained. For SVM classifier, the One-vs-One voting strategy was adopted, and the parameters, including radial basis kernel function parameter, error cost coefficient and relaxation coefficient were set to 0.0125, 60 and 0.001, respectively. By the classification of 8 kinds of pests, on the one hand, using the same SVM method, the test results showed the effectiveness of proposed HELM feature extraction. Texture features in wavelet domain were traditionally related to single scale low frequency lower order moments (SLM), including the mean, variance and the third order moment of low frequency image, multiscale low frequency lower order moments (MLM), multiscale high frequency lower order moments and low frequency lower order moments (HMLM), and LBP features for the low frequency image. Texture features in space domain were traditionally related to LBP, PCA and features based on gray-level co-occurrence matrix (GLCM). Compared with SVM recognition rates of the traditional texture features in wavelet domain and space domain, it was found that the proposed HELM feature had a higher recognition rate which were increased by at least 17 percentage points. In addition, the proposed HELM feature had moderate run time of 11.7 s containing from features extraction of 210 pest images to SVM classification of 8 kinds of typical pests. On the other hand, using the same HELM features and Hu moments, the test results showed the effectiveness of the proposed SVM recognition. For artificial neural network (ANN), three layers BP network structure was constructed and the sigmoid transfer function of hidden layer was selected. For Bayes classifier, Gaussian window function was used for estimating probability density. Compared with ANN run time, containing from the train for 105 pest images to the test for 105 pest images, the run time of the proposed SVM was 0.481s, nearly 2s less than ANN. Meanwhile, compared with ANN and Bayes recognition rates, the proposed SVM recognition rate was 97.5% , increasing at least 6 percentage points.