Speculation and negation information extraction is an important task and research focus in natural language processing (NLP). This paper proposes a two-layer bidirectional long short-term memory (LSTM) neural network model for speculation and negation scope detection. Firstly, a bidirectional LSTM neural network is utilized in the first layer to learn useful feature representations from the syntactic path which is from the cue to the token. Then, lexical features and syntactic path features are concatenated into the feature representations of the token. Finally, taking the scope detection problem as a sequence labeling task, another bidirectional LSTM neural network is employed in the second layer to identify the scope of the current cue. The experimental results show that the presented model is superior to other neural network models and attains excellent performances on BioScope corpus. Particularly, the model achieves the accuracy (percentage of correct scopes) of 86.20% and 80.28% on speculation and negation scope detection on Abstracts subcorpus, respectively.