Given the low quality and efficiency of situation element extraction in network security situation awareness techniques, this paper proposes a situation element identification model incorporating particle swarm optimization and simulated annealing (PSO-TSA). In the position update module, the Metropolis criterion is utilized to optimize the individual and global extremum in the PSO algorithm to increase the selectivity of the particles and improve the quality of the situation elements extraction. In the parameter optimization module, the parameters in the PSO algorithm are optimized using the Metropolis criterion, and the parameter optimization process and particle fitness are evaluated simultaneously to rid the local optimum and improve the efficiency of the situation element recognition. Due to the actual needs of the current network state, this paper selects 37 network security data fields and establishes a small network environment to obtain a more realistic network security dataset SDS-W. This paper conducts experiments of the situation element recognition on the open cybersecurity dataset and the SDS-W, respectively. Experiments show that PSO-TSA improves the accuracy of situation element recognition by an average of 5% to 7% while the time cost remains the same or even less.