When the smoke concentration of forest fire increases, the blurring degree of the corresponding image increases, and the total bounded variation gradual declines. Based on the characteristics of the variation, the difference between the boundaries can be effectively represented. Therefore, a detection method of forest fire smoke image is proposed based on total bounded variations. The objective function is extremum calculated with the idea of block stationary analysis, and the total bounded value is obtained. By comparing the total bounded variational value twice, the suspected smoke is extracted from the block result graph, and the fused clustering processing of feature data is used to obtain the final suspected smoke area. In order to get better smoke detection effect, it analyzes the motion characteristic of suspected smoke feature area, and the smoke area is judged by fusion, then the fire alarm is given. The algorithm shields the complex calculation of the static characteristics of the smoke. When the suspected characteristics of smoke are analyzed, the smoke can be accurately detected by only its motion characteristics, which avoids the errors caused by cumbersome calculation. The comparison and verification results show that the output of the algorithm is efficient and stable.