Road　safety　performance　function　(SPF)　analysis　using　data-driven　and　nonparametric　methods,　especially　recent　developed　deep　learning　approaches,　has　gained　increasing　achievements.　However,　due　to　the　learning　mechanisms　are　hidden　in　a　＂black　box＂　in　deep　learning,　traffic　features　extraction　and　intelligent　importance　analysis　are　still　unsolved　and　hard　to　generate.　This　paper　focuses　on　this　problem　using　a　deciphered　version　of　deep　neural　networks　(DNN),　one　of　the　most　popular　deep　learning　models.　This　approach　builds　on　visualization,　feature　importance　and　sensitivity　analysis,　can　evaluate　the　contributions　of　input　variables　on　model＇s　＂black　box＂　feature　learning　process　and　output　decision.　Firstly,　a　visual　feature　importance　(ViFI)　method　that　describes　the　importance　of　input　features　is　proposed　by　adopting　diagram　and　numerical-analysis.　Secondly,　by　observing　the　change　of　weights　using　ViFI　on　unsupervised　training　and　fine-tuning　of　DNN,　the　final　contributions　of　input　features　are　calculated　according　to　importance　equations　for　both　steps　that　we　proposed.　Sequentially,　a　case　study　based　on　a　road　SPF　analysis　is　demonstrated,　using　data　collected　from　a　major　Canadian　highway,　Highway　401.　The　proposed　method　allows　effective　deciphering　of　the　model＇s　inner　workings　and　allows　the　significant　features　to　be　identified　and　the　bad　features　to　be　eliminated.　Finally,　the　revised　dataset　is　used　in　crash　modeling　and　vehicle　collision　prediction,　and　the　testing　result　verifies　that　the　deciphered　and　revised　model　achieves　state-of-theart　performance.