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Short text similarity computing plays an important role in natural language processing, and it can be applied to many tasks. In recent years, there are lots of researches getting important results on natural language processing. Although there are some good results in English, there is no major breakthrough in Chinese. Different from the proposed methods, we reserve the Stop words in the training dataset of word vector for Chinese characteristics, and add the TongyiciCilin to the training data of the short text semantic similarity computation model. We compared the effect of Word2vec and Glove methods in our model. We use the Chinese short text semantic similarity dataset which is designed by Chinese grammar experts. The results show that the accuracy of the model is improved by 2%–3% by retaining Stop words in word vector training data and adding TongyiciCilin to training data. The accuracy of our model is better than Baidu short text similarity calculation platform on the same testing dataset.

The existing emergency rescue systems face the following problems: rescue systems belong to different rescue brigades and are independent of each other, there is no guarantee that the data interface is consistent with the National Safety Supervision Bureau, the bureau and provincial rescue centers can't get the important rescue information timely and accurately; In addition, the rescue brigades cannot share the large-scale equipment, human resources, training materials, and rescue prearranged planning cases to reduce the cost. We put forward the mine emergency rescue platform based on SOA, which supports national, provincial level and brigade level rescue management, with unified interface, entirely sharing of resources such as large equipment, online examination, simulation training, rescue prearranged planning case and so on to reduce the maintenance costs. The test results of platform show that the platform can easily support all the national emergency rescue management units/users to concurrent access, the throughput is 97.6 transactions/sec, the number of concurrent users is 2100, and 99% of transactions are finished in 81ms.