Summary: In order to improve the precision of compressed sensing (CS) sparse reconstruction algorithm, a CS reconstruction algorithm based on elastic collision optimization (ECO) and improved gradient pursuit strategy for wireless sensor networks (WSNs) is proposed. First of all, a new intelligent optimization computing technology: ECO is put forward. Referred to physical collision information interaction process, the historical optimal solution and population optimal solution are used to guide evolutionary direction and individuals are spread around the optimal solution in the form of \(N (0, 1)\), which helps to improve the convergent speed and extend the individual search space. Qualitative analysis shows that ECO can converge to the global optimal solution in probability 1 and the analysis of the diversity index shows that the algorithm has the ability of global optimization. Secondly, aiming at the defects of greedy reconstruction algorithm as low reconstruction efficiency and sparsity set in advance for high dimensional sparse signals, the ECO is applied to the CS reconstruction algorithm on the basis of the design validity index, and the quasi Newton gradient pursuit strategy is also introduced, which helps to realize the accurate reconstruction of large scale sparse data. Finally, simulation is carried out using multidimensional test functions and WSNs data acquisition environment. The simulation results show that ECO has certain advantages in convergent accuracy and success rate, and compared with other reconstruction algorithms, the reconstruction results are significantly improved for high dimensional sparse signal.