The invention discloses a lung CT image computer aided system and method based on cluster analysis. The method comprises extracting the nine textural features of a lung CT image including energy, entropy, correlation, difference moment, contrast, sum average, variance, difference, and difference average based on a gray-level co-occurrence matrix; dividing samples into a training set and a verification set according to a ratio of 3:1; subjecting original high-dimensional data to dimensionality reduction by using an improved U-reliefF feature weight calculation method and calculating the corresponding weight values of respective textural features; and applying the weight values to an improved Weightedk-means algorithm to construct a nodule classification model. The method finds, by combininga plurality of textural parameters, that the combination of energy, contrast, entropy and correlation has the best classification effect, achieves a recognition rate of 81.18% of benign nodules and 91.48% of malignant nodules, has a good benign and malignant nodules recognition rate, and contributes to the early diagnosis of the lung cancer.