A new fuzzy 3-partition entropy approach based on a fast recursive genetic algorithm was proposed to reduce the repeated computations and to improve the processing efficiency in extraction of FISH-labelled (Fluorescence In Situ Hybridization) genes. An iteration validation method was presented to determine the window width of the membership functions and the membership functions considering the boundary conditions and gray weights were selected to perform the fuzzy 3-partition. To improve the efficiency of selecting optimal thresholds, a recursive algorithm was presented to convert the computation of fuzzy entropy to a recursive process. Then, the no-repetitive results of the processing moments were stored for the succeeding genetic algorithm to compute the fitness of each individual. Finally, the optimal thresholds were searched by the genetic algorithm in a high speed. The result of the proposed algorithm was compared to those of the several common algorithms and the classification probability and run time were analyzed as the test criterion of optimal thresholds. By evaluating various types of simulated images and real FISH images, it shows that the run time of the proposed algorithm is 1% that of other common algorithms and the misclassification error is less than 6.00×10-2. These results demonstrate that the proposed algorithm is effective for improving the precision and efficiency of extracting FISH-labelled genes.