Aimed at the problem that the local optimization is easily caught in and the convergence rate is slow, this paper proposes a hybrid algorithm of particle swarm optimization and simulated annealing based on intuitional fuzzy entropy (IFEPSO-SA) in solving largescale 0-1 knapsack problems by using intelligence algorithm. Exchange operation and simulated annealing mechanism are applied to the local secondary optimization. Then, a metric based on intuitional fuzzy entropy (IFE) of the population is used to change inertia weight adaptively, and particles make the mutation based on the metric. The testing result shows that IFEPSO-SA is good in solution quality in solving largescale 0-1 knapsack problems. And the simulation experiment results show that entropy fluctuation of IFEPSO-SA is comparatively stable compared with the intuitional fuzzy entropy based particle swarm optimization (IFEPSO), reflecting a yet higher local search ability. Meanwhile, IFEPSO-SA is superior to IFEPSO and classical particle swarm optimization and simulated annealing in terms of convergence speed and solution quality.