A　better　knowledge-based　visual　question　answering　(KBVQA)　model　needs　to　rely　on　visual　features,　question　features,　and　related　external　knowledge　to　solve　an　open　visual　question　answering　task.　Although　the　existing　knowledge-based　visual　question　answering　works　have　achieved　some　accomplishments,　there　are　still　the　following　challenges:　1)　There　is　a　serious　lack　of　visual　feature　information.　Image　information　is　worth　a　thousand　words.　Only　relying　on　the　converted　salient　text　information　is　difficult　to　express　the　original　rich　information　of　the　image.　2)　The　external　knowledge　acquired　is　not　comprehensive　enough,　and　there　is　a　lack　of　relevant　knowledge　directly　retrieved　by　visual　feature　information.　To　solve　these　challenges,　we　propose　a　Visual　Information-Guided　knowledge-based　visual　question　answering　(VIG)　model.　It　fully　considers　the　utilization　of　visual　features　information.　Specifically:　1)　We　introduce　multi-granularity　visual　information　that　can　comprehensively　characterize　visual　feature　information.　2)　We　consider　not　only　the　knowledge　retrieved　through　text　information　but　also　the　knowledge　directly　retrieved　from　visual　feature　information.　Finally,　we　feed　the　visual　features　and　retrieved　multiple　text　knowledge　into　an　encoder-decoder　module　to　generate　an　answer.　We　perform　extensive　experiments　on　the　OKVQA　dataset　and　achieve　state-of-the-art　performance　of　60.27%　accuracy.