Aiming at the actual fusion needs of heterogeneous difference features collaborative optimization are often involved for dual-mode infrared images fusion， and the existing difference feature attributes cannot be targeted to adjust algorithms to drive fusion effectively， resulting in poor fusion effect， a method of infrared image fusion algorithm selection based on joint drop shadow of possibility distributions is proposed. Firstly， the fusion effectiveness of the difference feature amplitude of the dual-mode infrared image is calculated， and the probability density distribution of the difference feature amplitude and the distribution of the frequency attribute based on K-nearest neighbor method is obtained. Then the difference feature weight function through difference feature amplitude attribute and frequency attribute is constructed， and the possibility distribution synthesis between the heterogeneous difference feature weight function and fusion algorithms is established to obtain the joint drop shadow of the fusion effectiveness of heterogeneous difference feature weight function and the fusion algorithms. Finally， the fusion performance index is constructed to select the optimal fusion algorithm dynamically. The experimental results show that the optimal fusion algorithm selected by this method on the ranking score index is significantly higher than other algorithms， which the feasibility of applying joint drop shadow of possibility distributions in the optimal selection of dual-mode infrared images fusion algorithm is verified .