The development of network technology has further promoted the informatization of colleges and universities; the network teaching resources have been continuously enriched; the network teaching management has been constantly standardized; and the development of scientific computing technology also occurred. Network learning has become a major learning method and is accepted by more and more people. In order to build an intelligent network learning model, this paper starts from data mining, index data processing, and evaluation system design from the construction of index data as the main line. Based on the analysis of the existing network learning model index system, this paper proposes a boundary change network learning index system and uses data mining technology to mine the student status and student learning activity information recorded in the web page and the background database. In this way, the key factors which can evaluate students' online learning effect are extracted and quantified. Then, based on the basic principles and algorithms of fuzzy logic, artificial neural network, and fuzzy neural system, artificial neural network and adaptive fuzzy neural system are used for network learning modeling, respectively, and the experimental simulation and performance analysis of the two models are carried out. The experimental results show that the test accuracy rates of the two models based on BP neural network and adaptive fuzzy neural network are 68.6% and 91.4%, respectively. The model based on the adaptive fuzzy neural network is better in the field of network learning, is feasible in theory, and has reliable results in practice, and can be used as an intelligent computing model for developing network learning systems. It has a good promotion value and realizes the intelligence and automation of online learning, thereby further improving the online learning platform and promoting the construction and use of online courses.