In this era of intelligence, the learning methods of learners have substantially changed. Many learners choose to learn through online education platforms. Although learners may enjoy more high-quality educational resources, when they are faced with an abundance of resource information, they are prone to become lost in knowledge, among other problems. To solve this problem, a multi-algorithm collaborative, personalized, learning path recommendation model is proposed to provide learning guidance for learners of online learning platforms. First, the learner model is constructed from four perspectives: cognitive level, learning ability, learning style, and learning intensity. Second, the association rule algorithm is employed to generate a sequence of knowledge points and to plan the learning sequence of knowledge points for learners. Last, the swarm intelligence algorithm is utilized to ensure that each knowledge point is matched with personalized learning resources with a higher degree of adaptability so that learners can learn using a more targeted approach. The experimental results show that the research results of this paper can, to a certain extent, recommend ideal learning paths to target users, effectively improve the accuracy of recommended resources, and thus improve the learning quality and learning effect of users.

Cognitive diagnosis is one of the essential components in intelligent education and aims to diagnose student’s skill or knowledge mastery based on their responses. Recently, with the development of artificial intelligence, some researchers have applied neural network methods to cognitive diagnosis. Although they achieved some success, they seemed to lack a certain basis for designing network structures and could not obtain a unified method for designing network structures. We propose a neural network method for cognitive diagnosis based on Q-matrix constraints, introducing the Q-matrix from traditional cognitive diagnosis to enhance the reliability and interpretability of the network structure. Specifically, our method is highly consistent with generalized deterministic inputs, the noisy “and” gate model (GDINA), and the network structure reflects the direct contribution of skills to answering questions correctly, as well as the indirect contribution of interactions between skills to answering questions correctly. Finally, extensive experiments on both simulated and real datasets demonstrated that our method achieved high accuracy and reliability, with a particularly notable performance on low-quality datasets. As the number of questions and skills increased, our approach exhibited greater robustness compared to the classical methods, highlighting its potential for broad applicability in cognitive diagnosis tasks.