Phishing deceives users' trust through subtle URL and HTML disguises, stealing sensitive data or spreading malicious viruses. Phishing detection from URLs has been the focus of research in recent years, which balances the performance and time compared to list-based and content-based approaches. The approaches using neural networks to extract semantic information from URLs to detect phishing websites can avoid feature engineering. However, the features' plausibility cannot be verified. Heuristic features designed artificially can reflect URL differences more reasonably, but the current features lack diversity and have poor generalization in the real web environment. In this paper, we propose a phishing detection model combining heuristic features and machine learning, which extracts features from URL components and linguistics perspectives, leading to lightweight and feature diversity. Three datasets with significant differences are used to verify the model's generalizability. Eventually, the average accuracy of the three datasets reaches 98.68%, and the average precision, recall, and F1-score are all above 98%, which shows good generalizability and outperforms the baselines.