Adversarial examples can evaluate the robustness and safety of deep neural networks effectively. Aiming at the problem of low success rate of adversarial attacks in black-box scenarios and to improve the transferability of adversarial examples， a Multi-space Probability Enhancement Adversarial example generation Method （MPEAM） was proposed. The transferability of the adversarial examples was improved by the proposed method through introduction of two pieces of random data enhancement branches in the adversarial example generation method. In this process， random image Cropping and Padding （CP） based on the pixel space， as well as random Color Changing （CC） based on HSV color space， were implemented， respectively， by each branch. At the same time， the returned image examples were controlled by constructing a probability model， which increased the diversity of the original examples while decreasing the dependence of the adversarial examples on the original dataset， thereby enhancing the transferability of adversarial examples. On this basis， the proposed method was introduced into the integration model to further improve the success rate of the adversarial example attack in black-box scenarios. After extensive experiments on ImageNet dataset， the experimental results show that the proposed method improves the black-box attack success rate by 28.72 and 8.44 percentage points， averagely and respectively， compared to the benchmark methods Iterative Fast Gradient Sign Method （IFGSM） and Momentum Iterative Fast Gradient Sign Method （MIFGSM）， and improves the black-box attack success rate by up to 6.81 percentage points compared to the attack methods based on single-space probability enhancement. The above indicates that the proposed method can improve the transferability of adversarial examples at a small cost of complexity and achieve effective attacks in black-box scenarios.