Swarm Intelligence is the global intelligent behavior emerged from the interaction of groups of simple agents. From the view of complex systems, this paper comprehensively discusses the system structure, operation mechanism, modeling tools, algorithmic model and applications of swarm intelligence according to its fundamental principles. Firstly, around the system structure of swarm intelligence represented especially by ant colony and bird flock, the agents＇ attributes, their behavior rules and their interaction modes are analyzed, and then the feedback mechanisms and learning mechanisms implied in swarm intelligence are induced and revealed. Based on the intro- ductions to the commonly-used modeling tools such as Genetic Algorithm （GA）, Artificial Neural Network （ANN）, Cellular Automata （CA）, and Agent-Based Modeling （ABM）, etc. , four kinds of typical models and algorithms of swarm intelligence, viz. , ant colony foraging, ant clustering, labor division in ant colony, and bird flock foraging, are discussed in detail, aiming to conclude the general rules to model and simulate thecomplex systems based on swarm intelligence. Finally, the applications of swarm intelligence to engineering optimization, production management, robotics, data analysis and pattern recognition are introduced, and some perspectives on the development of swarm intelligence are made as the concluding remarks of this paper.