Research on fuzzy clustering of ideological and political MOOC resources under the background of 'Internet plus'

The massive, diverse, and complex structure of ideological and political learning materials on MOOC platforms results in low clustering accuracy and high time consumption.This study introduces a novel approach for categorising digital educational resources for political education within MOOC platforms through advanced computational techniques.The methodology begins with comprehensive data acquisition using web-based harvesting tools, followed by rigorous data refinement procedures to ensure information integrity.Subsequently, an enhanced convolution neural network architecture incorporating document embedding vectors was developed for textual feature representation.The framework integrates kernel-based fuzzy c-means clustering enhanced with bio-inspired optimisation techniques, specifically employing echolocation-inspired swarm intelligence as the initialisation mechanism for improved convergence.Empirical validation demonstrated superior performance metrics, achieving classification precision above 95% and retrieval effectiveness surpassing the 93% benchmark.