The amount of data in online practical training is relatively large, and there are certain risks in practical teaching, which makes the design of online practical training platforms difficult. However, feedforward neural networks can process and analyze a large amount of data, provide personalized learning paths and feedback for students, and conduct practical training in a virtual environment. Students can try various operations without any risks. Therefore, design a network training and teaching platform based on feedforward neural networks and virtual simulation. The hardware of the network training and teaching platform is designed using the B/S architecture, which simplifies development and maintenance through centralized server processing and unified browser access. At the same time, a hardware system consisting of a main control center platform, intelligent network devices, etc. is constructed by combining feedforward neural networks and virtual simulation technology. A realistic network training scene and virtual character model were constructed through virtual simulation technology, and fine modeling and texture processing were carried out using 3ds Max and Photoshop to achieve an immersive learning experience and improve learning interest. A feedforward neural network model was designed to enhance students’ practical abilities and innovative thinking. Through virtual training scenarios and customized teaching content, precise grasp of student learning characteristics and efficient provision of teaching resources were achieved. The experimental results show that all functions of the platform can operate normally and meet expectations. In terms of performance, it has high throughput and can meet the processing needs of most user requests.