Detection of the loose particles is urgently required in the Aerospace seal relay production processes. Particle impact noise detection (PIND) is a national aerospace electronic component loose particles detection method. Aiming at the misjudgment of the loose particles signal and component signal in the traditional detection method, this paper uses the parameteroptimized decision tree algorithm to classify the detection signal. After comparing the waveforms of the component signal and the loose particle signal in the time domain and the frequency domain, select the most representative feature as the split attribute of the decision tree. The grid search method is used to find the optimal splitting criterion and splitting depth of the decision tree, then use the parameter optimization decision tree to establish the classification model. The experimental results show that using the parameteroptimized decision tree algorithm to classify the loose particles detection signals can effectively improve the accuracy of the classification results, Gmeans value and Fmeasure value.