Large amount of data are exchanged and the internet is turning into twenty-first century Silk Road for data. Machine learning (ML) is the new area for the applications. The artificial intelligence (AI) is the field providing machines with intelligence. In the last decades, more developments have been made in the field of ML and deep learning. The technology and other advanced algorithms are implemented into more computational constrained devices. The online English test system based on ML breaks the shackles of the traditional paper English test and improves the efficiency of the English test. At the same time, it also maintains the fairness of English test and improves the marking speed. In order to realize an online English test system based on ML and facilitate the assessment of students’ college English courses, this paper mainly adopts relevant research and design on the main functional modules, key technologies, and functional realization of the online English test. The brand-new powerful teaching software and the online examination system can help schools to conduct more systematic and scientific management. The conclusion shows that as brand-new and powerful teaching software, the online examination system can help schools to conduct more systematic and scientific management.

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To align different ontologies, it is necessary to find effective ways to achieve interoperability of information in the context of the Semantic Web. The development of accurate and reliable techniques to automatically perform this task, it is becoming more and more crucial as overlap between ontologies grows proportionally. In order to solve the problem that traditional machine translation cannot meet the needs of users because of the slow translation speed. According to the characteristics of Ontology's domain knowledge concept system, deep neural network learning model based machine translation method is proposed. Through the experimental design, we examine the translation time and BLEU score and other indicators. After junior translators use the tools, the translation time is reduced by 34.0% and the BLEU score increases by 7.59; after the senior translators use the tools, the translation time is reduced by 11.3%, and the BLEU score is increased by 1.67. Analysis of the experimental results shows that the essence of this method is to complement translation skills, so it is more effective for junior translators who are not good enough in translation skills. The machine translation method based on deep neural network learning can significantly improve the quality and efficiency of translation.Povzetek: Predstavljena je metoda prevajanja s pomočjo globokih nevronskih mrež in ontologij.