# Neural Machine Translation of Electrical Engineering Based on Vector Fusion
## 1 Introduction  
Machine translation refers to the use of computers to achieve automatic translation technology from one natural language text to another natural language text under the premise of maintaining semantic equivalence [1]. It is an important research direction combining natural language processing and artificial intelligence. With the continuous development of economic globalization, international communication has become increasingly frequent, so it is inevitable that there will be many problems with language barriers. Regarding these \"language barriers\" that hinder people's communication, traditional manual translation can no longer meet the growing demand for translation, although its accuracy and translation quality are guaranteed. Moreover, for the translation of some professional fields, translators should not only master linguistic knowledge but also know some professional knowledge of the field to be translated in order to obtain appropriate translation results. In the face of the shortcomings of human translation, such as high cost, few resources and long time, people have the idea of using machines to replace human translation. With the rapid development of computers, communication and other technologies in recent years, machine translation came into being and has made a series of progress. Although the translation results are not as good as manual translation, machine translation itself also has the advantages of fast translation speed, low cost, convenient use, and can meet the needs of the public. It can better solve the \"language barrier\" problem of people who use different languages when communicating, which is of great significance for promoting national unity, strengthening cultural exchanges, and promoting foreign trade. Therefore, how to do a better job of machine translation is a necessary research issue to make its effect closer and closer to manual translation and even make the final translation effect reach \"faithfulness, expressiveness, and elegance\". The development of machine translation can be mainly divided into three stages: rule-based machine translation [2], statistical machine translation [3] and neural machine translation [4; 5; 6; 7; 8; 9]. Neural machine translation, as the current mainstream method of machine translation, has achieved good translation results for general corpora, but it still faces great challenges for machine translation in low-resource fields such as professional fields [10; 11]. For machine translation in the professional field, it is not only necessary to master the general technology of machine translation but also to have a basic understanding and mastery of the professional field of translation, including many professional words and technologies in the text, which should be more suitable for the meaning and habits of the professional translation. In particular, some professional words may have very different meanings in different fields, so it is necessary to use the corpus of professional fields to train translation models in specific fields. In the current social and economic development, electrical engineering involves all aspects of our lives. Machine translation in the field of electrical engineering has great significance in promoting the progress and development of the industry. The model translation results trained by using an electrical corpus will have stronger professional characteristics and can effectively meet the translation needs of professionals in the field of electrical engineering when performing tasks such as document reading and equipment operation. In addition, for this specific field of translation, machine translation will have obvious advantages in translation speed, so it is necessary to do a good job of neural machine translation in the field of electrical engineering.