Acoustical scene classification technology plays an important role in daily life by analyzing its recording environment through the audio recorded in public areas. Different from the traditional classification problem in which there is no relationship between classes, there is an implicit hierarchical structure relationship between the classes of acoustic scene classification (parent class and subclass). For example, the parent class of the airport and shopping mall is indoor. However, the existing methods do not consider this characteristic of acoustic scene classification task and ignore the dependency relationship between the parent class and the subclass. Therefore, an acoustic scene classification method is proposed, which is based on hierarchical information fusion by using the hierarchical structure relationship between acoustic scene classes. In this method, two separate classifiers are designed to classify the parent class and the subclass respectively. The information of the parent class is fused in the process of the subclass classification, and the hierarchical dependency loss is designed to punish the predicted mismatch between the parent class and the subclass. The experimental results on TAU urban acoustic scenes 2020 mobile development dataset show that the method based on hierarchical information fusion effectively improves the performance of the acoustic scene classification model with an increase of 1.1% in classification accuracy.