Cross-domain sequential recommendation (CDSR) aims to enhance recommendation performance in a target domain by leveraging user sequential preferences from a source domain. As most advanced methods employ various neural networks to model item-ID sequences, they often struggle with a sparse data. Recent text-based CDSR approaches utilize rich text information to facilitate knowledge transfer across domains but still encounter three main challenges: (i) how to capture domain-shared semantic relationships; (ii) how to effectively integrate semantic intents and behavior intents; and (iii) how to design sufficient supervision signals for model training. In this paper, we propose a novel text-enhanced CDSR solution, i.e., contrastive text-enhanced Transformer (CTT), which jointly learns user preferences from semantic and behavioral information. Specifically, our CTT is a dual-view structure, including an inter-domain preference view and an intra-domain preference view. In the former, we design a domain-shared cross-attention mechanism to encode semantic correlations across different domains by an attention map, which is then utilized for cross-domain semantic intent learning. In the latter, we extract fine-grained behavior intents from item-ID sequences and coarse-grained semantic intents from item-text sequences, thereby capturing high-quality intra-domain preferences. Our CTT integrates these two types of preferences to predict the next item for each user. Moreover, we propose a semantic-behavior contrastive learning approach to enhance cross-domain preference learning and knowledge transfer. Extensive experiments on three real-world datasets demonstrate that our CTT outperforms the state-of-the-art models by an average of 5.86% on HR@5. The scripts for data preprocessing and conducting experiments, the parameter configurations, and the source codes of our CTT and all baselines are available at https://github.com/XinbeiCai/CTT.