In　order　to　improve　the　detection　precision　of　internal　defect　in　the　ancient　wooden　structures,　defect　simulation　tests　on　pine　and　elm　commonly　used　in　ancient　buildings　were　performed　by　using　stress　wave　detection　and　drilling　resistance　detection.　Based　on　detection　data,　three　typical　evaluation　criteria,　which　are　the　information　entropy,　the　correlation　coefficient,　and　residual　sum　of　squares,　were　selected　as　a　priori　information.　Combining　with　the　expert＇s　fuzzy　evaluation　value,　Bayesian　formula　was　used　to　modify　the　prior　information　to　determine　the　weight　coefficients　of　the　two　detection　methods,　and　a　combined　prediction　model　was　established.　The　results　show　that　the　combination　of　subjectivity　and　objectivity　enables　the　revised　weights　to　more　reasonably　and　accurately　reflect　the　relative　importance　of　each　detection　method　in　prediction　evaluation,　which　reduces　the　forecasting　error.　Specifically　speaking,　the　mean　error　of　the　combined　model　was　reduced　by　49.8%　and　59.8%,　respectively,　compared　with　stress　wave　detection　and　drilling　resistance　detection.　Moreover,　the　five　error　indicators　of　this　combined　forecasting　model　are　the　smallest　in　all　methods,　indicating　the　proposed　method　has　a　better　forecasting　effect.　It　provides　an　effective　application　tool　for　the　practice　of　forecasting　the　internal　defects　of　wooden　components　in　ancient　buildings.