Automating the verification of building information models for compliance with fire safety regulations remains a pressing issue in the architectural and construction industry. Existing automated verification systems rely on rule-based approaches that ignore the building's topological context and are poorly adapted to new projects. This paper examines the development and experimental verification of methods for predicting door fire protection parameters in BIM models using graph neural networks and validating the approach using real-world design data from seven residential buildings provided by a major real estate developer. A methodology for predicting door fire resistance classes based on relational graph convolutional networks is proposed, along with a pipeline for extracting data from a specialized format, constructing a graph, and generating features that take into account geometric, semantic, and topological characteristics. Experiments were conducted to predict the presence and fire resistance class with cross-project validation using the "one building out of sample" principle. The developed approach enables automated verification of fire protection parameters and reduces the time required to analyze building models. The use of graph neural networks ensures that topological context is taken into account and high prediction accuracy is achieved, while the use of real data from a major real estate developer confirms the practical applicability of the method.