统筹规划城镇化布局是新时期国土空间规划政策的重要组成部分。明确不同空间的主要功能并划分城市功能区，对于优化土地开发模式具有重要意义。本文从数据挖掘的角度识别与分析城市功能区，所得结果与实际情况相符。研究以代表性出租车轨迹数据为基础，选取成都绕城高速范围内的滴滴出行轨迹数据，首先生成轨迹时间序列数据，并利用动态时间规整（DTW）算法构建时间序列相似性矩阵；其次，采用K-中心点聚类算法生成土地聚类的初步结果，并选取分类精度较高的样本作为训练样本；随后，基于DTW的K近邻（KNN）分类算法用于城市功能区的分类与识别；最后，结合兴趣点（POI）辅助分析，获得成都市最终的功能布局。结果表明，成都市的空间结构复杂，城市功能相互交织，但仍遵循一定规律。此外，交通流量与人流数据相较于简单的出租车上下车数据更能反映居民出行规律。原始DTW计算方法具有较高的时间复杂度，可通过归一化及降低时间序列维度进行优化。半监督学习分类方法同样适用于轨迹数据，最佳选择应为

Overall scientific planning of urbanization layout is an important component of the new period of land spatial planning policies. Defining the main functions of different spaces and dividing urban functional areas are of great significance for optimizing the land development pattern. This article identifies and analyses urban functional areas from the perspective of data mining. The results of this method are consistent with the actual situation. In this paper, representative taxi trajectory data are selected as the research basis of urban functional areas. First, based on trajectory data from Didi Chuxing within the high-speed road surrounding Chengdu, we generated trajectory time sequence data and used the dynamic time warping (DTW) algorithm to generate a time series similarity matrix. Second, we utilized the K-medoid clustering algorithm to generate preliminary results of land clustering and selected the results with high classification accuracy as the training samples. Then, the k-nearest neighbour (KNN) classification algorithm based on DTW was performed to classify and identify the urban functional areas. Finally, with the help of point-of-interest (POI) auxiliary analysis, the final functional layout in Chengdu was obtained. The results show that the spatial structure of Chengdu is complex and that the urban functions are interlaced, but there are still rules that are followed. Moreover, traffic volume and inflow data can better reflect the travel rules of residents than simple taxi on–off data. The original DTW calculation method has high temporal complexity, which can be improved by normalization and the reduction of time series dimensionality. The semi-supervised learning classification method is also applicable to trajectory data, and it is best to select training samples from unsupervised learning. This method can provide a theoretical basis for urban land planning and has auxiliary and guiding value for urbanization layout in the context of land spatial planning policies in the new era.