DASFA-FCM proceed as follows: ① Initialization parameters: γ, Tmax, m, generate initial population ${X}_{i}\left(i=1,2,\cdots ,n\right)$ , n indicates all micro-blog texts, k represents the number of initial cluster centers, initializing the position of each firefly. ② Calculating the influence value A(Xi) of each firefly according to Formula (9). ③ Calculating similarity between two texts(comparison of each micro-blog text and class center). when $sim\left(i,j\right)<\epsilon$ , the value of β0, uij are 0; when $sim\left(i,j\right)\ge \epsilon$ , all are 1. In this moment, the mutual attraction between fireflies is calculated according to Formula (2). ④ According to Formula (7), calculating the dynamic adaptive step length under the current iteration. ⑤ Calculating fitness function F(Xi), F(Xj); if F(Xi) < F(Xj), it shows that the firefly i influence is bigger than j, firefly i is in a better position than j, so firefly j moves to i, update each firefly position according to Formula (8). ⑥ Repeating steps ③ to ⑤ until the maximum number of iteration is reached. We can get the center of the cluster with the most influential fireflies. The number of the cluster center is C. ⑦ Based on the initial class centers found above, calculating the cluster center and membership matrix. ⑧ Calculating the distance ${d}_{ic}=‖{x}_{i}-{x}_{c}‖$ from the micro-blog text i to the cluster c, and classifying topics into the nearest cluster center. ⑨ Repeating steps ⑦ and steps ⑧. If the termination condition is reached, the location and influence of the most influential firefly will be output, and the result after clustering, otherwise continue. ⑩ We get hot topics based on the arrangement of influence values, output the top 50% topics.