An Efficient Big Data Visualization Deep Learning Architecture Model for Path Selection of College Students through Moral Education

Visualization technology can be used to present the analysis results in a more intuitive and easy-to-understand way, which help educators better understand moral education needs of college students, adjust their teaching strategies accordingly. The combination big data visualization also improve efficiency effectiveness colleges universities. research on path selection students based has great significance for promoting development universities, cultivating high-quality talent with good character. This paper proposed an Optimization model analytics education. associated information are stored cloud data. process is performed optimization feature extraction. integrated Flamingo weighted black widow model. stated as Integrated Black Widow (IFBW) performance IFBW implemented deep learning Restricted Boltzmann Machine (RBM) architecture. Simulation that achieves higher classification accuracy rate 99% minimal error rate.