Relations extraction is a widely researched topic in nature language processing. However, most of the work in the literature concentrate on the methods that are dealing with single relation between two named entities. In the task of multiple relations extraction, traditional statistic-based methods have difficulties in selecting features and improving the performance of extraction model. In this paper, we presented formal definitions of multiple entities and multiple relations and put forward three labeling methods which were used to label entity categories, relation categories and relation conditions. We also proposed a novel relation extraction model which is based on dynamic long short-term memory network. To train our model, entity feature, entity position feature and part of speech feature are used together. These features are used to describe complex relations and improve the performance of relation extraction model. In the experiments, we classified the corpus into three sets which are composed of 0–20 words, 20–35 words and 35+ words sentences. On conll04.corp, the final precision, recall rate and F-measure reached 72.9, 70.8 and 67.9% respectively.