Airwriting using mobile phones has many applications in human-computer interaction. However, the recognition of airwriting character needs a lot of training data from user, which brings great difficulties to the pratical application. The model learnt from a specific person often cannot yield satisfied results when used on another person. The data gap between people is mainly caused by the following factors: personal writing styles, mobile phone sensors, and ways to hold mobile phones. To address the cross-people problem, we propose a deep neural network(DNN) that combines convolutional neural network(CNN) and bilateral long short-term memory(BLSTM). In each layer of the network, we also add an AdaBN layer which is able to increase the generalization ability of the DNN. Different from the original AdaBN method, we explore the feasibility for semi-supervised learning. We implement it to our design and conduct comprehensive experiments. The evaluation results show that our system can achieve an accuracy of 99% for recognition and an improvement of 10% on average for transfer learning between various factors such as people, devices and postures. To the best of our knowledge, our work is the first to implement cross-people airwriting recognition via motion sensor signal, which is a fundamental step towards ubiquitous sensing.