Anomaly detection in industrial chains is crucial for preventing industrial chain risks. Unlike traditional time series data, industrial chain data possesses characteristics such as temporal evolution, heterogeneous distribution, complex association structures, and non-uniform sampling, making anomaly detection particularly challenging. Existing methods are primarily designed for static homogeneous networks and uniform time sampling scenarios, making it difficult to effectively capture abnormal evolution patterns in heterogeneous association structures within non-uniform time series. To address these challenges, this paper proposes a novel Temporal Heterogeneous Graph Learning-based Anomaly Detection framework (THeGraL-AD) that leverages node and semantic attention mechanisms to enhance neural controlled differential equations for effectively capturing industrial chain evolution patterns. Specifically, we first construct a temporal heterogeneous evolution graph that integrates static topological structures with dynamic spatiotemporal dependencies. Subsequently, we design a neural controlled differential equation module combined with a dual graph attention mechanism to extract node evolution features. An adaptive threshold strategy is then utilized to detect anomalous nodes. Experiments demonstrate that THeGraL-AD outperforms baseline methods on three datasets, achieving more accurate anomaly detection.