As an open source project hosting platform

GitHub participates in the development of open source projects with multi-developers. As the core element of GitHub

developers ensure the activity of the whole system. However

many new projects can not find suitable collaborative developers in a short time and the development cycle gets delayed. To solve this problem

this paper proposes a CNN-LSTM developer project recommendation model based on Word2Vec

which trains developers to access the project sequence by Word2Vec

and vectorizes the project

calculates the project similarity with CNN-LSTM model

and recommends the appropriate project sequence for developers. Through the project prediction and similar project discovery experiments based on 62

031 developers' project access data in GitHub in 2015

the experimental results show that the model has better recommendation effect and can help developers find similar projects of interest.