Considering the low positioning accuracy and strong scene dependence of optimization strategy in the Distance Vector Hop （DV-Hop） localization model， an improved DV-Hop model， Function correction Distance Vector Hop （FuncDV-Hop） based on function analysis and determining coefficients by simulation was presented. First， the average hop distance， distance estimation， and least square error in the DV-Hop model were analyzed. The following concepts were introduced： undetermined coefficient optimization， step function segmentation experiment， weight function approach using equivalent points， and modified maximum likelihood estimation. Then， in order to design control trials， the number of nodes， the proportion of beacon nodes， the communication radius， the number of beacon nodes， and the number of unknown nodes were all designed for multi-scenario comparison experiments by using the control variable technique. Finally， the experiment was split into two phases：determining coefficients by simulation and integrated optimization testing. Compared with the original DV-Hop model， the positioning accuracy of the final improved strategy is improved by 23.70%-75.76%， and the average optimization rate is 57.23%. The experimental results show that， the optimization rate of FuncDV-Hop model is up to 50.73%， compared with the DV-Hop model based on genetic algorithm and neurodynamic improvement， the positioning accuracy of FuncDV-Hop model is increased by 0.55%-18.77%. The proposed model does not introduce other parameters， does not increase the protocol overhead of Wireless Sensor Networks （WSN）， and effectively improves the positioning accuracy.