Abstract: In order to assist the professional designers of subway engineering to quickly obtain the reference model matching the current design requirements from the BIM model case library, a BIM model hybrid recommendation algorithm based on feature matching is proposed. Firstly, the feature data is obtained from BIM model based on the secondary development of Revit. Secondly, the entropy weight grey correlation model is used to calculate the recommendation degree of the model instance by using the basic information such as the model feature parameters. Then, the recommendation degree of the model instance is calculated by using the fusion model of gradient boosting decision tree algorithm (GBDT) and logical regression (LR) algorithm combined with the user interaction data. Finally, the combination proportion of the two recommenders can be adjusted dynamically according to the scale of the training data set. Experiments show that this method not only avoids the problem of cold start, but also has better BIM model recommendation quality with the support of enough user interaction data.

Key words: BIM model, grey correlation analysis, entropy weight, LR, GBDT