Under the development trend of intelligent deformation, color change, temperature change, and spectrum change technology, low-feature targets accelerate the realization of feature fusion with the background of natural features, which makes the detection and evaluation of low-scattering, micro-reflection, and weak-radiation targets under complex natural backgrounds more and more difficult . Furthermore, the detection method, rapid decision-making and accurate evaluation of potential threat targets in certain situations have become difficult problems. This paper has proposed a parameter model for Fusion Degree(FD) between targets and background environments to improve the selection efficiency of multi-feature detection algorithms and the accuracy of the detection effect evaluation under the fusion scene of complex natural background environment with low-feature targets, such as discrete targets, camouflage targets, small targets, abnormal targets and so on. At the same time, simulated image data of 4 different spectral feature distribution scenes were designed, including vegetation camouflage targets embedded in the grass background, vegetation camouflage targets embedded in the soil background, vegetation and cement road camouflage targets embedded in the soil background, and vegetation, cement road, and soil camouflage targets embedded in the grass, cement road, and soil background respectively. Furthermore, signal noise ratio(SNR) of 200, 400 and 800 were applied to the spectral feature distribution scenes in which vegetation camouflage targets were embedded in the grass background. Through comprehensive Testal analysis of multiple factors such as spectrum information of targets, spectrum information of background, data noise ratio, etc., the research on threat evaluation of multi-feature detection algorithm was carried out, which was based on FD model between target and environment. Under the condition that the standard deviation is less than 0.08, the average values of FD parameters of the 9 classic multi-feature detection algorithms such as MtACE, MtAMF, MtCEM, SumACE, SumAMF, SumCEM, WtaACE, WtaAMF, and WtaCEM for the detection results of the 4 spectrum distribution scenes are 0.320 0, 0.350 2, 0.862 4, 0.365 8, 0.365 8, 0.846 1, 0.680 0, 0.680 0, 0.948 2, respectively. Meanwhile on the condition that the standard deviation is less than 0.07, the average values of FD parameters of the 9 classic multi-feature detection algorithms for detection results of 3 different levels of noise ratio data are 0.313 5, 0.320 9, 0.774 7, 0.369 6, 0.369 6, 0.847 5, 0.695 6, 0.695 6, 0.960 3, respectively. In this paper, through the analysis of detection and fusion evaluation tests under different spectrum distribution scenarios and different noise levels, the threat level ranking of multi-feature detection algorithms is realized, and the detection efficiency of multiple low-feature targets in complex scenarios is greatly improved. Considering spectrum and noise factors, for the detection of discretely distributed low-feature targets in complex scenes, the priority order of the 9 classic multi-feature detection algorithms is: MtACE>MtAMF>SumACE=SumAMF>>WtaACE=WtaAMF>MtCEM>SumCEM>WtaCEM.