Different from the classical probability density estimator construction strategies based on the Parzen window method， we propose a heuristic kernel density estimator （HKDE） based on nearest neighbor error measurement function， to improve the accuracy of fitting probability density function of modal-proximity data. From the perspective of data and model uncertainties， we analyze the defects of traditional kernel density estimators in solving the problem of probability density estimation of modal-proximity data. The heuristic probability density values that can reduce the uncertainty of observed data are obtained by referring to the convergence of probability density values with respect to the histogram box width. Based on the heuristic probability density value， we construct the sophisticated objective function to determine the optimal bandwidth for kernel density estimator by reducing the model uncertainty. Extensive experiments on 18 modal-proximity datasets are conducted to validate the feasibility， rationality and effectiveness of the designed HKDE. Results show that HKDE can obtain a better approximate performance of probability distribution than seven existing representative probability density function estimators. HKDE has lower estimation error and closer probability density function estimates to the real density values than other kernel density estimators.