Aviation　fuel　cost　is　one　of　the　most　important　cost　expenditures　for　airline　companies,　exerting　huge　impact　on　the　profit　margin.　Trip　fuel　accounts　for　the　highest　proportion　of　aviation　fuel;　therefore,　reasonable　prediction　and　optimization　of　the　trip　fuel　volume　can　effectively　reduce　the　extra　fuel　carrying　volume　of　the　flight,　thereby　improving　the　fuel　economy　and　effective　commercial　consumption　of　the　fleet.　However,　some　limitations　exist　in　previous　fuel　prediction　studies.　For　example,　the　data　source,　the　applicable　operation　scenarios　and　aircraft　models　are　relatively　limited,　making　them　difficult　to　popularize.　More　importantly,　previous　studies　have　not　solved　the　problem　of　flight　safety　risk　preference.　Therefore,　combined　with　the　problem　characteristics　of　flight　trip　fuel　volume　prediction,　a　model　for　flight　trip　fuel　volume　prediction　based　on　the　random　forest　algorithm　considering　risk　preference　adjustment　is　proposed.　The　model　selects　the　characteristic　items　according　to　the　flight　plan,　operation　environment,　aircraft　configuration　and　performance,　and　adds　the　model　evaluation　index　reflecting　economy　and　safety　on　the　basis　of　mathematical　characteristics.　Then,　the　model　is　fitted　and　tested　with　the　actual　operation　data　from　the　routes　of　an　airline　company.　The　experimental　results　show　that　the　model　can　complete　the　prediction　with　expected　accuracy　and　practical　significance,　and　has　better　performance　than　the　previous　commonly　used　models.　The　research　results　have　been　applied　in　an　airline　company,　providing　important　reference　for　flight　plan　making,　dispatcher　refueling,　as　well　as　energy　saving　and　emission　reduction　analyses.　©　2022,　Beihang　University　Aerospace　Knowledge　Press.　All　right　reserved.