A method for offline hand-written Chinese character recognition is proposed based on partial cascade feature classification, which is of much research value and highly innovative. Two feature extracting algorithms are proposed as follows: weighted Low Threshold Hough Space Sampling(wHHS) and Histogram of Local Binary Distribution(HLBD). These algorithms can map images of various sizes into vectors with fixed dimension, but eliminate the disadvantages of existing algorithms, which has high sensitivity of the distribution of strokes destiny, and demand uniformization. A strategy of classification based on partial cascade feature is proposed and the relationship between number of category for classification and accuracy is put forward with the corresponding mathematical proof.