Design and Implementation of Multi-modal Teaching Resources Retrieval Algorithm Model Based on Random Forest

This article introduces the design and implementation of a multimodal teaching resource retrieval algorithm model based on random forests. With the continuous development of educational technology, the quantity and diversity of teaching resources are constantly increasing. How to efficiently retrieve and match resources that are suitable for teaching needs has become an important issue. This study proposes a multimodal teaching resource retrieval algorithm model based on random forests, aiming to provide accurate and personalized teaching resource recommendations. The algorithm model consists of the following main steps. Firstly, collect and organize characteristic data of teaching resources, such as keywords, themes, difficulty, etc. Then, based on the user’s query conditions and needs, multiple feature vectors of different patterns are constructed and trained using the random forest algorithm. Random forest is an ensemble learning method that can effectively process data with multimodal features and generate accurate classification and recommendation results. During the training process, techniques such as cross validation and feature importance assessment are utilized to optimize and select models. The experimental results show that the multi-mode teaching resource retrieval algorithm model based on random forest has good performance in terms of accuracy and personalization.