The authors design recognition features with the consideration of medical field characteristic for the online medical text, and the experiment of the entity recognition is carried out on the self-built data set. Concerned about five common diseases: gastritis, lung cancer, asthma, hypertension and diabetes. In the experiment, an advanced machine learning model Conditional Random Field is used for training and testing. The target entities include five kinds: disease, symptoms, drugs, treatment methods and check. The effectiveness of the proposed features is verified by using the experimental method, and the accuracy of the total 81.26% is obtained and the recall rate is 60.18%. Subsequently, the further analysis is given for the recognition features.

Based on the research results of the non-linear dynamic model which consists of a plane network system built by spring-slider-damper, cellular automata model is developed from 2D to 3D, with the evolution rule modified. The authors design 100×100×40 grids for 3D cellular automata model, and produce 13540 synthetic seismic events. The computation results show that the tempo-spatial distribution of 3D synthetic events is consistent with that of actual natural earthquakes, and frequency-magnitude of synthetic seismic events meets G-R relation. Moreover, the synthetic events by cellular automata have fractal distribution on tempo-spatial scale. Compared with 2-D model, 3D cellular automata is more reasonable.