To　investigate　the　effects　of　temperature　and　biomass　concentration　of　Hydrothermal　liquefaction　(HTL)　on　chemical　properties　of　biocrudes,　machine　learning　(ML)　was　used　to　predict　the　weight　of　hydration　parameters　on　the　properties　of　biocrudes.　The　elemental　compositions,　molecular　weights,　functional　groups,　thermal　degradation,　molecular　structure　of　biocrudes　were　studied.　The　optimum　yield　of　biocrudes　was　65%　and　the　highest　heat　value　reached　up　to　34.28　kJ/g,　showing　comparable　fuel　properties.　It　was　found　that　the　hydration　temperature　significantly　affects　the　elemental　components,　functional　groups　and　molecular　weight　and　structures　of　biocrudes.　In　addition,　biomass　concentration　also　affect　the　functional　groups　and　structures　of　biocrudes.　ML　results　indicated　that　Support　Vector　Machine　Linear　Kernel　method　is　suitable　for　heat　value　prediction.