Recognizing　the　associations　among　entities　in　corporate　reports　accurately　is　crucial　for　market　regulation　and　policy　development.　Nevertheless,　confronted　with　massive　corporate　information,　the　traditional　manual　screening　approach　is　cumbersome,　struggling　to　match　the　demand.　Consequently,　we　propose　a　multimodal　network　model　incorporating　conceptual　semantic　knowledge　injection,　CSKINet,　for　accurately　extracting　relations　from　Chinese　corporate　reports.　The　essential　highlights　in　the　design　of　the　CSKINet　model　are　the　following:　(1)　Integrate　the　conceptual　descriptions　of　corporations　from　external　resources　to　construct　the　semantic　knowledge　repository　of　corporate　concepts,　which　provides　a　solid　semantic　foundation　for　the　model.　(2)　Multimodal　features　are　extracted　from　the　documents　by　various　means　and　corporate　conceptual　knowledge　is　integrated　into　the　model　representation　to　enhance　the　representation　capability　of　the　model.　(3)　The　multimodal　self-attention　mechanism　that　captures　cross-modal　associations　and　the　biaffine　classifier　with　Taylor　polynomial　loss　function　that　optimizes　training　iterations　further　improve　the　learning　efficiency　and　prediction　accuracy.　The　results　on　the　real　corporate　report　dataset　show　that　our　proposed　model　can　more　accurately　extract　the　relations　from　Chinese　corporate　reports　compared　to　other　baseline　models,　where　the　F1　score　reaches　85.76%.