The problem of positioning wireless sensor networks is an important and challenging topic in all walks of life. Inspired by the evolution behavior of natural plant communities and traditional positioning algorithms, a novel positioning algorithm based on the behavior of artificial plant communities is designed and presented here. First, a mathematical model of the artificial plant community is established. Artificial plant communities survive in habitable places rich in water and nutrients, offering the best feasible solution to the problem of positioning a wireless sensor network; otherwise, they leave the non-habitable area, abandoning the feasible solution with poor fitness. Second, an artificial plant community algorithm is presented to solve the positioning problems encountered in a wireless sensor network. The artificial plant community algorithm includes three basic operations, namely seeding, growing, and fruiting. Unlike traditional artificial intelligence algorithms, which always have a fixed population size and only one fitness comparison per iteration, the artificial plant community algorithm has a variable population size and three fitness comparisons per iteration. After seeding by an original population size, the population size decreases during growth, as only the individuals with high fitness can survive, while the individuals with low fitness die. In fruiting, the population size recovers, and the individuals with higher fitness can learn from each other and produce more fruits. The optimal solution in each iterative computing process can be preserved as a parthenogenesis fruit for the next seeding operation. When seeding again, the fruits with high fitness can survive and be seeded, while the fruits with low fitness die, and a small number of new seeds are generated through random seeding. Through the continuous cycle of these three basic operations, the artificial plant community can use a fitness function to obtain accurate solutions to positioning problems in limited time. Third, experiments are conducted using different random networks, and the results verify that the proposed positioning algorithms can obtain good positioning accuracy with a small amount of computation, which is suitable for wireless sensor nodes with limited computing resources. Finally, the full text is summarized, and the technical deficiencies and future research directions are presented.