In order to adapt to the changes of users' needs, search engines need to be optimized. This study optimized the search engine by improving the performance of the topic crawler, designed a search engine based on intelligent algorithm, calculated the relevance of the topic by vector space model (VSM) method, optimized the search performance of the crawler by combining gray wolf optimizer (GWO) algorithm, and carried out experiments taking keywords of "education", "entertainment", and "art" as examples. The results showed that the accuracy of the method was 75.33% when the number of pages captured was 32.000, 21.76% higher than that of the ACO algorithm, the average accuracy of the three keywords was 76.26%, the average topic relevance was 35.09% higher than the ACO algorithm, and the coverage was also high. The experimental results show that the search engine designed in this study has better performance in web search and can be further applied in practice.
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