Due to the widespread use of the Internet, users have the ease of accessing collections of university academic documents stored in virtual libraries whose information is of an unstructured type. In recent years, the production and publication of scientific documents in Ecuador have increased considerably, so the search and classification of documents is a fundamental task within information retrieval computer systems. Intelligent search systems allow found information with a high degree of accuracy and similarity. For the development of this project, academic documents from the Ecuador Network of Open Access Repositories (RRAAE) were retrieved using a glossary of terms in the area of science and technology. For the recovery of documents, the web scraping technique was used and its results were stored in a cloud database in JSON format. In the recovered documents, NLP techniques were applied to clean and homogenize the unstructured information. Two similarity metrics were used to measure the divergence between the retrieved documents, and similarity matrices were generated based on the title, keywords, and abstract, which were then unified into a weighted matrix. The results of the system are displayed in a web interface that, through the use of graphs, shows the relationship between the linked documents. The operation of the similarity system was validated through functional tests through experimentation with a collection of 30 queries with indexed and non-indexed terms in the input of the information retrieval system. The experiments showed that for indexed terms, the system performs better.