Leukocyte image feature extraction and classification are studied to improve the correct recognition rate of leukocyte image. For cell texture feature extraction, leukocyte image texture features are extracted by using the improved local fuzzy pattern,and the texture feature extraction method based on local fussy pattern (LFP) was proposed by making the threshold parameter fuzzy in local binary pattern (LBP). The algorithm introduces in uniform pattern to make the extracted feature dimension decrease to 10 with rotation invariance. The classification of 100 CellAtlas’s white blood cells images was tested with a support vector machine combination classifier established by a directed acyclic graph method. Experimental results show that: the improved local fuzzy pattern algorithm simplifies texture feature quantity to realize “discard the false and retain the true”.The leukocyte image classification and recognition with noise exhibits excellent performance, so that the extracted features have better Robustness. And it has a short running time, high efficiency, leukocyte correct recognition rate is up to 93%. Improved support vector machine classifier shows efficient classification effect,and has better characteristics to small sample analysis.