Quantifying　the　influence　of　uncertainty　on　gold　prices　is　significant　for　improving　related　financial　decision　making.　This　study　proposes　a　novel　CNN-LSTM　neural　network　that　can　extract　potential　features　from　sample　data　to　effectively　predict　gold　prices.　Specifically,　we　demonstrate　various　uncertainty　measures　containing　market　volatility　information,　such　as　the　economic　policy　uncertainty　index　(EPU),　epidemic　stock　market　volatility　index　(IDEMV),　and　volatility　index　(VIX),　which　can　contribute　to　the　prediction　of　gold　prices　rather　than　relying　solely　on　the　history　of　tickers,　which　is　conventionally　used　for　prediction.　In　addition,　the　proposed　model　is　evaluated　against　SVR　and　two　different　LSTM　models.　The　empirical　findings　reveal　that　incorporating　additional　features,　such　as　uncertainty　measures,　contributes　to　improving　the　predictive　accuracy　of　the　model.　The　CNN-LSTM　model,　with　the　inclusion　of　EPU,　IDEMV,　and　both,　achieves　a　high　prediction　accuracy.　Additionally,　the　overall　prediction　accuracy　of　the　CNN-LSTM　model　outperforms　the　other　proposed　methods.　The　findings　provide　profound　insight　into　portfolio　diversification　and　risk　management　practices　for　governments　and　businesses.