Log-based　anomaly　detection　is　crucial　for　software　reliability　assurance.　System　logs　are　semi-structured　data　containing　constant　and　variable　contents,　both　of　which　can　provide　valuable　features　for　anomaly　detection.　Due　to　variables　being　heterogeneous　and　discrete,　there　is　a　lack　of　effective　approaches　that　can　comprehensively　incorporate　features　of　variables　into　log-based　anomaly　detection.　In　this　paper,　we　propose　VCRLog,　an　anomaly　detection　method　that　mines　the　relationships　among　the　heterogeneous　and　discrete　variables　and　extracts　important　features　contributing　to　anomaly　detection.　Firstly,　considering　parsing　methods　cannot　accurately　extract　variables　from　logs,　we　propose　a　variable　extraction　method　based　on　domain　knowledge.　Secondly,　to　capture　and　extract　the　relationship　feature　among　heterogeneous　and　discrete　variables,　we　design　a　conceptual　model　based　on　system　operation　to　construct　variable　attributed　graph,　which　can　mine　important　feature　vectors　by　structural　embeddings.　Finally,　considering　constants　directly　express　the　meaning　of　logs,　we　combine　relationship　vectors　with　semantic　vectors　of　constants　to　achieve　transformer-based　anomaly　detection.　Experimental　results　show　that　our　proposed　method　can　accurately　detect　anomalies　and　maintain　high　accuracy　as　the　training　data　size　decreases,　outperforming　existing　methods.　Our　source　code　and　experimental　data　are　publicly　available　at　https://github.com/Fridaywjy/VCRLog.　©　2024　IEEE.