To address the limitations of existing multiscale information diffusion prediction models， which ignore the dynamic characteristic of cascade propagation and exhibit limited performance in independent microscopic information prediction， a Multiscale Information Diffusion prediction model based on HyperGraph Neural Network （MIDHGNN）was proposed. Firstly， Graph Convolutional Network （GCN） was used to extract user social relationship features from the social network graphs， while HyperGraph Neural Network （HGNN）was used to extract global user preference features from propagation cascade graphs. These two types of features were fused to enable microscopic information diffusion prediction. Secondly， Gated Recurrent Unit （GRU） was employed to sequentially predict potential spreaders until reaching virtual users. The cumulative number of predicted users at each step was regarded as the determined cascade size for macroscopic propagation forecasting. Finally， a Reinforcement Learning （RL） framework using policy gradient to optimize parameters significantly enhanced macroscopic information diffusion prediction performance. For microscopic information diffusion prediction， compared to the suboptimal model， MIDHGNN achieves average improvements of 12.01%， 11.64%， and 9.74% in Hits@k on Twitter， Douban， and Android datasets， respectively， and average improvements of 31.31%， 14.85%， and 13.24% in mAP@k. For macroscopic prediction， MIDHGNN reduces the Mean Squared Logarithmic Error （MSLE） by at least 8.10%， 12.61%， and 3.24% on these three datasets， respectively， with all metrics significantly outperforming the comparison models， validating its effectiveness.