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The reasonable classification of spare-parts is the basis of establishing the prediction model, the data of a certain warship spare-parts is less and the classification index factor is insufficient, so it is easy to use the traditional method to produce the problem of overfitting. A Monte Carlo K-means algorithm is proposed, and the sample variance is used for the spare-parts consumption volatility cluster analysis. Firstly, using Monte Carlo to calculate the initial clustering center, and refers to the SBC method to formulate the number of clustering categories k. The classification of instrument spare-parts is optimized by variance clustering modeling, and finally the cluster results of instrument spare-parts are obtained. The example shows that the method can effectively improve the classification results of K-means method without considering other index factors.It is suitable for the model with too small amount of data and white noise.