为进一步提升基于钻进参数的围岩智能分级模型精度，综合考虑钻进参数间耦合影响作用、围岩地质非均一性等因素影响，从进给速度、推进压力、打击压力、回转压力等原始钻进参数特征变量出发，通过特征组合、统计方式，构建多变量的钻进参数特征体系并进行特征重要性评估。然后利用6种常见的机器学习方法进行围岩智能分级应用，并比较分析特征挖掘前后不同围岩级别样本类间距离及分级模型准确率。结果显示： 相比原始特征，多变量特征体系下不同围岩级别样本类间距离均值提升66.09%~85.41%，各模型分级总体准确率由75.5%~87.5%提高到 90.0%~92.5%，表明基于钻进参数多变量特征体系对围岩分级精度有很好的提升作用。

To improve the accuracy of the intelligent classification model based on drilling parameters, the interaction between drilling parameters and the geological heterogeneity of the surrounding rock is taken into account. Key variables such as penetration velocity, feed pressure, hammer pressure, and rotation pressure are used as the original drilling parameter features. These features are then integrated into a multivariable drilling parameter characteristic system using feature combination and statistical methods. Six machine learning methods are applied for rock classification, with a comparative analysis conducted on the interclass distances of rock grade samples and the accuracy of the classification models, both before and after feature extraction. The results reveal significant improvements with the multivariable feature system. The average interclass distance of different rock grade samples increases by 66. 09% to 85.41%, while the overall accuracy of the classification models rises from 75.5%－87.5% to 90.0%－92.5%. These findings demonstrate that the multivariable drilling parameter feature system can significantly improve the performance of rock classification.