Predicting online video popularity faces a critical challenge: prediction drift, where models trained on historical data rapidly degrade due to evolving viral trends and user behaviors. To address this temporal distribution shift, we propose an Anchored Multi-modal Clustering and Feature Generation (AMCFG) framework that discovers temporally-invariant patterns across data distributions. Our approach employs multi-modal clustering to reveal content structure, then leverages Large Language Models (LLMs) to generate semantic Anchor Features-high-level concepts such as audience demographics, content themes, and engagement patterns-that transcend superficial trend variations. These semantic anchors, combined with cluster-derived statistical features, enable prediction based on stable principles rather than ephemeral signals. Experiments demonstrate that AMCFG significantly enhances both predictive accuracy and temporal robustness, achieving superior performance on out-of-distribution data and providing a viable solution for real-world video popularity prediction.

In this research, an Individual Learner Big Data Based Cloud Role Player Game Learning Corresponding to Individual Learner Big Data is proposed. Based on the proposed platform, all the learning states and information (Big Data) of individual learner can be further analyzed. By using dynamic assigni ng algorithm, individual learner (student) can be separated into different levels related to the learning ability. According to dynamic assigning algorithm, student's answering times and answering correct rate of each education scenario in the proposed cloud based digital game learning system is considered. Then, suitable teaching scenario will be provided for individual user corresponding to personal learning ability. Each designed modular teaching scenario can be embedded into the game platform on the virtual machine in cloud. The information about students in the game learning system can be stored in the cloud database. In addition, the whole learning system is established as a virtual machine. System maintainer can configure the learning system easily and quickly. Based on cloud, different remote devices can connect to server for learning.