## 1 Introduction  
With the development and progress of social productivity and technology, the Internet is playing an increasingly important role. The continuous development of the Internet and the Internet of Things has brought exponential explosion of information, which is also the problem of information overload. It is under this background that the recommendation system comes into being, aiming to solve the problem of recommending appropriate items for users under massive information. It mainly studies the characteristics of users and items, and generates a better recommendation list by mining the potential or explicit links between users and items, so as to make recommendations for users. In this case, the recommendation accuracy becomes the most important factor to measure the quality of a recommendation system or model, and then becomes the core pursued in all stages of the development of the recommendation system. The recommendation system is composed of three important modules: user modeling module, recommendation object modeling module and recommendation algorithm modeling module. Among them, recommendation algorithm modeling, also called recommendation model, is the core of the research. Recommended system model in the decades of development, there has been a qualitative leap, in the early days of the recommended model, the traditional collaborative filtering algorithm [1] undoubtedly occupies very important position, the recommendation algorithm based on user [2] and recommendation algorithm based on item [3] for a long time to occupy the mainstream of the recommendation algorithm, until now also doing well. The proposal of matrix factorization [4] provides the basis for the development of factorization machine [5], and logistic regression [6] based on classification problems has also become the core algorithm of classification problem model. Recommendation system has realized the real booming development after enter the deep learning time, the concept of the model is more important, the neural network is put forward and the development laid the foundation of deep learning model, Wide & Deep [7] model combines generalization and memorization, for the first time for subsequent model provides a new train of thought, to the recent time, image processing has become the hot of current recommender systems. The continuous evolution and development of CNN [8] and RNN [9] continue to promote the progress of image processing. With the continuous development of the deep learning model, the complexity of the model is increasing. With the improvement of accuracy, the time complexity is also increasing, which has become one of the problems of the recommendation system model. There are many sources of time complexity. From a macro perspective, many hidden layers are added to the recommendation system model under deep learning. With the increasing of \"depth\", the data processing time is prolonged; On the other hand, the use of Embedding technology [10] greatly increases the data processing time of the model, which is the main source of the time complexity of the model; Finally, the increasing size of the data set also causes the model to need more time to process the data. However, few models actively pay attention to solving the problem of speed, and the problem of time complexity is usually considered only when two models are compared. For example, the promotion and use of FNN [11] are precise because its model is relatively simple, and FM method is used to initialize parameters for the model, and the model has strong interpretability. However, FNN model itself is difficult to automatically extract information carried by high-order combination features, which means that it is not ideal for processing large data sets. Therefore, our research on model speed starts from the three factors mentioned before, and finally improves the model speed through processing of data, Embedding technology and model structure.