岩体质量分级是地下工程初期设计和施工的基础。为了更加高效准确地开展岩体质量评价，提出了一种基于白鲸优化（BWO）随机森林的岩体质量评价模型——BWO-RF模型，同时构建了麻雀搜索算法优化随机森林（SSA-RF）、粒子群优化随机森林（PSO-RF）和未优化随机森林（RF）的岩体质量评价模型进行对比。在模型构建前，建立了包含131组工程实例数据的数据库，运用该数据库最终完成了4种模型的训练和测试。基于模型测试结果，采用准确率、查准率、召回率、F1值和AUC值5个评价指标对模型进行对比优选。研究结果表明：BWO-RF模型各项评价指标均优于其余3种模型，具有更优的评价性能；经过工程实例验证，本研究所提出的BWO-RF模型预测准确率达90%，可为实际工程建设提供参考依据，具备实际工程应用价值。

Rock mass quality classification is the foundation of initial underground engineering design and construction.In order to evaluate rock mass quality more accurately，this study used beluga whale optimization（BWO）to optimize random forest model（RF），a BWO-RF model which can be used for rock mass quality evaluation was proposed.At the same time，the rock mass quality evaluation models of sparrow search al-gorithm optimized random forest（SSA-RF），particle swarm optimization optimized random forest（PSO-RF）and non-optimized random forest（RF） were constructed for comparison.Before the models construction，a data-base containing 131 engineering cases data was established through literature review and field test data collec-tion.After writing the code of models construction，the training and testing of the four models were completed by using the database.Based on the model test results，five model evaluation indexes，accuracy，precision，recall，F1 score and AUC，were used to compare and select the best model of the four kinds of rock mass quality eva-luation models.The results show that the BWO-RF model has the best performance among the four kinds of rock mass quality evaluation models，and each evaluation indexes of model are better than the other three mo-dels，indicating that the BWO-RF model has better practicability in the evaluation of rock mass quality.Through the test set，the prediction accuracy of BWO-RF model proposed in this study is 90%，which can provide a reliable reference for practical engineering construction and has practical engineering application value.