With the rapid development of the Internet and the World Wide Web, and the increasing amounts and variety of information on the Internet, people can now use search engines to obtain a diverse rich range of information. This paper proposes a user intent prediction search engine system (UIPSES) based on query history, using machine learning and deep learning image recognition technologies. Two different search methods are developed, based on a user keyword search and an upload image file search. The upload image file search uses deep learning image recognition technology to obtain multiple intent features for the image. Both the keyword and image searches use machine learning technology to extract multiple search intent feature information from the search logs, which is used as a basis for creating a user intent prediction for the keyword information search and the image file search. UIPSES provides highly correlated website index information between user browsing and predicted intent behaviour and uses machine learning to periodically train each user search process to update the user search intent recognition model to adapt to changes in the user intent, to improve the overall inference performance and analyse the accuracy of UIPSES, and to realise a search engine system with personalisation and a high-quality user experience. The UIPSES is a novel image search system that compares the relevance of search engine results for image and text information by using mean average precision with the well-known advanced web image search engines (Google, Bing, and Yandex). When the user uploads an image file for a search, the highest mean average precision value achieved by these three web image search engines was 2.28% for image information and text information feedback. In contrast, UIPSES can adapt to different conditions for single-object or multi-object images searches by obtaining multiple features from images and making inferences based on search logs, and therefore achieves high mean average precision values of 82.57 and 98.28%. UIPSES can also accurately find preset image and text information with higher relevance to allow users to search for images. [ABSTRACT FROM AUTHOR]