A method combining power spectrum information entropy and GK fuzzy clustering was proposed to recognize the denatured biological tissue. The characteristics of porcine muscle tissues were changed by highintensity focused ultrasound irradiation. In this experiment, the thermistor was used to measure the temperature in the acoustic focal zone, and the ultrasonic echo signals at different temperatures were collected. In the data process, the original signal was intercepted and the influence of the segmentation number on the identification performance of power spectral information entropy was discussed. It was found that while the number of segments is 26 to 32, the accuracy, sensitivity and specificity of the power spectrum information entropy are higher. The power spectrum information entropy being calculated as the number of segments is 30 .The average value of the power spectrum information entropy from the signal corresponding to denatured tissues was about 0.094 higher than normal tissues, which was about 7.99%. When power spectrum information entropy is used as the characteristic parameter, the GK fuzzy clustering effect is better than the fuzzy Cmeans clustering. With GK fuzzy clustering, power spectrum information entropy has higher recognition rate than wavelet entropy.