Knowledge Tracing (KT), a fundamental technology in online intelligent education systems, is designed to model learners’ learning processes and monitor the dynamic evolution of their knowledge states. Learners’ memory of acquired knowledge decays over time, with forgetting patterns varying based on individual cognitive characteristics. However, most existing KT models adopt a unified and simplified forgetting function, which fails to accurately capture individualized memory decay and distinguish between the effects of content similarity and time on learning performance, leading to a significant decline in accuracy when predicting long-term learning outcomes. To address this, we proposes a Memory-Enhanced Personalized Diagnostic Knowledge Tracing model (MLEKT), which integrates a forgetting-aware linear bias, error-boosted spaced repetition algorithm, and genetic algorithm optimization to precisely model forgetting behaviors in long learning sequences. Specifically, this paper first designs a forgetting-enhancement module based on a spaced repetition algorithm to provide more fine-grained and personalized forgetting enhancement for different types of learning interactions. Second, a forgetting-aware linear bias mechanism is introduced to effectively distinguish the effects of content similarity and time. Finally, a genetic algorithm-based optimization method for personalized forgetting enhancement values is proposed, enabling personalized parameter configurations for different learners. Experiments on three public datasets demonstrate that the MLEKT model outperforms baseline methods in both accuracy and stability, with significant advantages in long-sequence learning interactions.