Research on Online Ideological and Political Teaching System Based on Web Technology and Feedforward Neural Network Model

With the incorporation of information technology into education, ideological and political education in universities and colleges has been changed dramatically. This study discusses designing and developing an online ideological and political teaching system-based Feed forward Neural Network. Through overcoming the disadvantages of classical classroom teaching-low motivation, inflexible content presentation, and limited personalized interaction-this system not only improves teaching efficiency but also increases students' participation. The system as proposed uses HTML5, CSS3, JavaScript, and server technologies like MySQL, and RESTful APIs to provide a responsive, scalable, and user-friendly platform along with Feed forward Neural Network model (FFNN). It includes interactive course materials, student tracking, and real-time forums to facilitate an integrated teaching-learning process. By experimental result accuracy 98% shows that this implementation has better feedback, the system indicated enhanced engagement, adaptability, and efficiency in ideological and political education.