According to the less accessibility characteristics for the detection of defects will result in detection ineffective and quantitative inaccurate. The study focused on the subject of aluminum plate，based on infrared nondestructive testing technology, combined with principal component analysis and probabilistic neural network(PNN)on the normal area and three kinds of cavity defects area for the recognition and area of quantitative evaluation. Firstly, research during the cooling process of heating aluminum plate，the initial characteristics were obtained from the sequence grey value of normal and three kinds of cavity defects area on the basis of sequence infrared image. And the principal component analysis was used to extract initial characteristics. Finally, combined with the probabilistic neural network, the cavity defects were identified and quantitatively evaluated in pixels. And the support vector machine was used to carry on the comparative study. Experimental results show that the evaluation accuracy rates of the normal and the three kinds of cavity defects area were 99.6%, 97.0%, 94.7% and 93.0% respectively, compared with the evaluation results of support vector machine, the proposed research method has higher accuracy. Research demonstrates that using principal component analysis and PNN, based on the temporal characteristics, to achieve the effectiveness and accuracy of the cavity defects identification and quantitative analysis of the area in units of pixels.