The quality text of nuclear power equipment describes the quality defects and other issues that occur during the design， procurement， construction， and commissioning stages of nuclear power equipment. Due to the different frequencies of quality events occurring at different stages， and the existence of the same keywords and similar expressions in quality texts corresponding to the same equipment at different stages， an improved recurrent pooling network classification model was proposed by integrating regularization and feedback for focus loss function to address the quality text classification problems with imbalanced number of categories and semantic description coupling. Firstly， BERT （Bidirectional Encoder Representation from Transformers） was used to convert nuclear power equipment quality text into word vectors. Then， an improved three-layer recurrent pooling network classification model structure was proposed， which expanded the extraction space for parameter training by adding intermediate layers and selecting appropriate weights， and enhanced the ability to represent semantic features of quality defects. Next， regularization and feedback for focus loss function was proposed to train the parameters of the proposed classification model. To solve the problem of uneven gradient bias of imbalanced samples during the training process， the regularization term was used to make the gradient change of the loss function more stable， and the feedback term was used to iteratively adjust the loss function based on the error between the true value and the predicted value. Finally， the corresponding stages of nuclear power equipment quality events were calculated using a normalized exponential function. On the real dataset of a certain nuclear power company and a public dataset， F1 value of this model was 2 percentage points and 1 percentage point respectively higher than that of Fast_Text network. The experimental results show that the proposed model has high accuracy in text classification tasks.