The core idea of machine power reduction is to train a large number of existing literature data into the machine learning model, so that it can understand the structure and semantics of the text, and thus judge the similarity between the paper to be searched and the existing literature. By reducing the weight of the machine, students and researchers can more easily detect whether their papers have problems such as duplication and plagiarism.

At present, many weight detection systems have adopted machine weight reduction technology and achieved good results. When dealing with large-scale literature databases, these systems can quickly and accurately find out the documents similar to the repeated papers to be detected, and give the corresponding similarity scores. At the same time, these systems also have humanized interfaces and diversified duplicate checking reports, which can meet the different needs of users.

However, machine weight reduction is not perfect. Although the machine learning model can learn the characteristics of a large number of literature data, it may be misjudged in the face of highly complex or innovative papers. In addition, the accuracy of machine weight reduction system is limited by the quality and quantity of training data, which needs to be updated and improved continuously.

To sum up, machine weight reduction can effectively reduce the duplicate checking rate of papers. However, in practical application, other factors need to be considered comprehensively, such as manual review and in-depth understanding of the text of the paper, in order to improve the accuracy and reliability of duplicate checking. In the future, with the continuous development and innovation of artificial intelligence technology, machine weight reduction is expected to become an efficient and intelligent duplicate checking method, providing a better guarantee for academic research.