Based on observed data, density estimation is the construction of an estimate of an unobservable underlying probability density function. With the development of data collection technology, real-time streaming data becomes the main subject of many related tasks. It has the properties of that high throughput, high generation speed, and the underlying distribution of data may change over time. However, for the traditional density estimation algorithms, parametric methods make unrealistic assumptions on the estimated density function while non-parametric ones suffer from the unacceptable time and space complexity. Therefore, neither parametric nor non-parametric ones could scale well to meet the requirements of streaming data environment. In this study, based on the analysis of the learning strategy in competitive learning, it is proposed a novel online density estimation algorithm to accomplish the task of density estimation for such streaming data. And it is also pointed out that it has pretty close relationship with the Gaussian mixture model. Finally, the proposed algorithm is compared with the existing density estimation algorithms. The experimental results show that the proposed algorithm could obtain better estimates compared with the existing online algorithm, and also get comparable estimation performance compared with state-of-the-art offline density estimation algorithms.