Paper similarity detection is a technique used to evaluate the similarity between two papers. It can help the author to ensure the originality of the paper and avoid copying the achievements of others. At present, there are many methods that can be used to detect the similarity of papers, including text comparison, statistics and machine learning.

Methods based on text comparison usually use cosine similarity or Jacques similarity to compare the similarity between two papers. These methods usually need to convert papers into vector representations, and then calculate the similarity between vectors. This method is simple and easy to use, but it may not capture more complex semantic relations in the paper.

The method based on statistics evaluates the similarity between the two papers by analyzing their lexical distribution, syntactic structure and semantic information. This method usually requires in-depth language analysis of the paper, so it requires a large amount of calculation. However, it can capture the language features in the paper more accurately, thus providing more accurate similarity detection results. The model can predict the similarity between the input paper and other papers according to the content of the paper. This method usually needs a lot of labeled data for training, and the model needs to be optimized to obtain the best performance. However, once the training is completed, this method can quickly and accurately detect the similarity between the two papers.