This work proposes an improved DV-Hop model based on function analysisand simulation parameter determination (named FuncDV-Hop) to address theproblems of low positioning accuracy and strong scene dependence of the DV-Hop localization model. The undetermined coefficient optimization, step functionsegmentation experiment, weight function strategy with equivalent points, andmaximum likelihood estimation correction are introduced to reduce the positioning error by analyzing the error reasons in the average hop distance, distanceestimation, and least square calculation of the DV-Hop model. The experiment isdesigned using the control variable method. The total number of nodes, the ratioof beacon nodes, the communication radius, the number of beacon nodes, and thenumber of unknown nodes are designed as variables to control the experiment.Finally, the experiments of two stages of simulation parameter determinationand integration optimization are carried out. The simulation optimization rateof all scenes is between 23.70% and 75.76%, and the average optimization rateis 57.23%. Experimental results show that the FuncDV-Hop model has the highest optimization rate of more than 50% in all experimental scenarios comparedwith the other models, the localization error is reduced by more than 0.1, andthe optimization rate is increased by more than 10% in the record parameters ofthe existing wireless sensor network system.