Teaching Mode of Mechanical Manufacturing Specialty Based on the Network Teaching Platform

The network teaching platform based on information technology is a challenge to the traditional teaching mode, and it is also an inevitable product of the development and maturity of information technology. This kind of learning platform is an online virtual learning environment that relies on computers and the Internet. It fully integrates mechanical related curriculum resources and has the advantages of good interactivity, strong immersion, and easy production and use. The purpose of this article is to study the teaching mode of mechanical manufacturing specialty under the network teaching platform. This article analyzes and compares the key contents such as the method of building a network virtual platform and the working principle of X3D virtual reality technology, and determines the key technology and overall plan for building a network teaching platform. This article uses CATIA to set and optimize the part model and generate file types that can be recognized by the X3D language to ensure a reasonable configuration of the start and stop time of each component in the motion mechanism, as well as the specific motion trajectory and rotation angle. Based on the interactive, immersive, easy-to-make and use virtual reality technology, this article integrates mechanical related professional knowledge resources to achieve the overall platform construction. It greatly improves the user's perception of the part model, is intuitive, flexible, and convenient, and truly gives the user a virtual and realistic operating experience, and finally realizes the dynamic interaction and perceptual communication between humans and models in a three-dimensional scene. Investigation and research show that in the analysis of the correlation between the learning input of mechanical manufacturing students and their own learning preparation under the online teaching platform, there is a significant positive correlation between student learning preparation and behavioral input, emotional input and cognitive input (p <; 0.001).