Knowledge　Tracing　(KT)　aims　to　track　the　evolving　knowledge　states　of　students　based　on　their　historical　performance,　playing　a　vital　role　in　online　intelligent　education　systems.　While　deep　learning-based　knowledge　tracing　achieves　impressive　predictive　performance,　existing　methods　suffer　from　two　main　shortcomings.　Firstly,　storing　massive　amounts　of　historical　information　introduces　irrelevant　noise　during　the　training　process.　Additionally,　individual　differences　may　prevent　the　model　from　accurately　capturing　students　comprehensive　states.　Secondly,　deep　learning　models　lack　interpretability,　failing　to　provide　precise　descriptions　of　students　knowledge　states.　This　paper　proposes　an　improved　knowledge　tracing　method　through　learning　processes　and　concept　similarity　map　(LCKT).　We　incorporate　diverse　features,　including　exercise,　exercise　difficulty,　concept,　response　time,　response,　and　interval　time,　to　measure　the　diversity　of　exercise　interactions.　Additionally,　we　utilize　a　forgetting　gate　to　simulate　the　decline　of　students　knowledge　over　time　during　the　learning　process.　Furthermore,　we　introduce　a　concept　similarity　map　as　a　constraint　for　model　training,　which　clearly　delineates　students　mastery　across　different　knowledge　points.　Extensive　experiments　on　three　real-world　datasets　demonstrate　that　LCKT　outperforms　state-of-the-art　KT　methods　and　exhibits　interpretability　to　some　extent.　©　2024　IEEE.