In general, web 2.0 technology serves as an educational tool for teaching and learning aspects. The study is aimed at exploring the interactive system of football teaching in the information technology era. The coach and players will utilize the mobile learning resources to get effective learning about the fun. Using mobile learning technology, the coach has to implement different modes to make the players learn about the game. The study implemented the convolutional neural network (CNN) algorithm to evaluate the accuracy of using web 2.0 technology to cooperative learning environment system design of football teaching. The results show that the network teaching interactive learning system of football courses based on web 2.0 can achieve the intended function of the college educational administration management system.

Every country is developing under the concept of artificial intelligence. Many countries are already working on student monitoring systems that allow them to control the student’s mentality and analyze each student’s behavior with the help of a wireless headband. There is certain high-tech education within the country in a maximum number of schools, which can be considered robotic monetization. With the help of tiny robots inside the classrooms, each student’s activeness and engagement level in the classes are captured and submitted to the teacher. All these practical applications help us imagine that there will be a massive response to artificial intelligence in the future of this world. On the other hand, sports management is a critical issue to consider for the country’s growth. This research evaluates the quality of football teaching by implementing an Artificial Neural Network model for online mode of education. The proposed model functions with the implementation of Association Rule Mining (ARM) in the intelligent system to monitor the activities of the player by training with the Artificial Neural Network (ANN). The proposed model is compared with the existing K-Mean algorithm, and it is observed that the proposed model has achieved an accurate evaluation of 99.6%.