In view of the problem that the existing researches do not consider the refinement of the factors affecting the network negative energy propagation and construct a propagation dynamics model for analysis, a Weak-Strong-Received-Infected-Evil (WSRIE) model of network negative energy propagation was proposed. Firstly, considering the difference of negative energy immunity and propagation ability of network users, the vulnerable states were divided into weak immunity and strong immunity, and the infectious states were divided into weak infection, strong infection and malicious propagation with unchanged scale. Secondly, according to the negative energy infection mechanism of the network, the state transition law was proposed. Finally, a dynamics model of network negative energy propagation for complex networks was constructed. The simulation comparison experiments on NW small world network and BA scale-free network were carried out. The simulation results show that under the same parameters, the weak immune node density of the NW network is 9 percentage points lower than that of the BA network, indicating that the network with small world characteristics is more susceptible to negative energy. In the BA network, the density of infected nodes with the malicious node degree of 200 is 5 percentage points higher than that with the node degree of 0, indicating that the greater the node degree of the network red opinion leader, the more network users affected by the network negative energy.

Traditional automatic text summarization has generally no word count requirements while many social network platforms have word count limitation. Balanced performance is hardly obtained in short text summarization by traditional digest technology because of the limitation of word count. In view of this problem, a new automatic short text summarization method was proposed. Firstly, the values of relationship mapping, length mapping, title mapping and position mapping were calculated to respectively form some sets of candidate sentences. Secondly, the candidate sentences sets were mapped to abstract sentences set by multiple mapping strategies according to series of multiple mapping rules, and the recall ratio was increased by putting central sentences into the set of abstract sentences. The experimental results show that multiple mappings can obtain stable performance in short text summarization, the F measures of ROUGE-1 and ROUGE-2 tests are 0.49 and 0.35 respectively, which are better than the average level of NLP&CC2015 evaluation, proving the effectiveness of the method.