This project describes a novel intelligent E-mail reply system through information retrieval and information generation techniques. There are several difficulties to realise different kinds of functions using machine learning and deep learning algorithms. For example, the publicly available raw training datasets cannot meet the functional requirements of the model, and the information generation class models cannot satisfy the long text-based predictions due to limitations of the algorithm. It is well known that the Term Frequency-Inverse Document Frequency (TF-IDF) model is one of the most widely used feature extraction methods in information retrieval because of its simple algorithm and excellent performance. Meanwhile, The Document to Vector (Doc2Vec) model is an extension algorithm of Word to Vector (Word2Vec), which can train the index of documents together based on turning words into vectors. Good results have been achieved in determining the relationship between words within a document, as well as the correlation between different documents. Recently, the Gated Recurrent Unit (GRU) model is playing an increasingly important role in natural language processing (NLP) as an advanced method of applying a recurrent neural network (RNN). Also, the GRU model utilises deep neural networks to predict and generate information instead of extracting the original existing information. Specifically, we use these three algorithms to train and implement our models after heavily processing our training data. Experimental results show that a hybrid model combining the GRU information generation model as the base with the method of sentence to vector embedding (Sent2Vec) is a practicable method for long-text prediction. In the end, an intelligent E-mail reply system is implemented in our experiment. Three models are compared through subjective human evaluation.