  AI-generated text detection plays an increasingly important role in various
fields. In this study, we developed an efficient AI-generated text detection
model based on the BERT algorithm, which provides new ideas and methods for
solving related problems. In the data preprocessing stage, a series of steps
were taken to process the text, including operations such as converting to
lowercase, word splitting, removing stop words, stemming extraction, removing
digits, and eliminating redundant spaces, to ensure data quality and accuracy.
By dividing the dataset into a training set and a test set in the ratio of 60%
and 40%, and observing the changes in the accuracy and loss values during the
training process, we found that the model performed well during the training
process. The accuracy increases steadily from the initial 94.78% to 99.72%,
while the loss value decreases from 0.261 to 0.021 and converges gradually,
which indicates that the BERT model is able to detect AI-generated text with
high accuracy and the prediction results are gradually approaching the real
classification results. Further analysis of the results of the training and
test sets reveals that in terms of loss value, the average loss of the training
set is 0.0565, while the average loss of the test set is 0.0917, showing a
slightly higher loss value. As for the accuracy, the average accuracy of the
training set reaches 98.1%, while the average accuracy of the test set is
97.71%, which is not much different from each other, indicating that the model
has good generalisation ability. In conclusion, the AI-generated text detection
model based on the BERT algorithm proposed in this study shows high accuracy
and stability in experiments, providing an effective solution for related
fields.
