The Buddha taught the dharma with a variety of dialects or languages. Afterward, the teachings of the Buddha were preserved orally for a long time before being eventually written down. With the spread of Buddhism, the Buddhist texts were translated into many different languages. The Buddhist texts were translated into Chinese since the Han Dynasty and then began to be translated into Tibetan during the Tang Dynasty. In modern times, as Buddhism spread to Western countries, the Buddhist texts were translated into many Western languages. Language is an important tool of smooth communication between people. Today, online translation tools make learning language and communication with each other faster and more convenient. At present, the development of deep learning in artificial intelligence greatly improves the precision of the automatic translation system. To achieve acceptable translation performance, these methods require a corpus with a large number of parallel sentences in both languages for training. However, although there are many Buddhist texts in different languages, it still lacks a well-constructed parallel sentence aligned corpus. Therefore, this thesis studies the method of the unsupervised sentence alignment and finds an appropriate algorithm to efficiently deal the sentence alignment of all Chinese-English Buddhist texts. In this study, for evaluations, several sutras with both Chinese and English versions are selected, such as some of the sutras in the "Chang Ahan Jing (Dīrgha Āgama)" and the "Foshuo Amituo Jing" from the "Taishō Shinshū Daizōkyō". Chinese and English texts are separated into sentences, and then segmented as words. For Chinese words, the English explanations are gathered from Chinese-English dictionaries to transform the Chinese words into English terms. Next, each sentence with words is transformed as a vector. To measure the similarity between two sentences now is regarded as the similarity of the two vectors. With the similarity measurement between two sentences, we adopt an alignment algorithm based on dynamic programming to generate the optimal sentence alignment results. The results of the experiment are evaluated in precision and recall through two standards: rigid and relax. The evaluation results show that the average of the rigid precision, rigid recall, rigid F1-measure, relax precision, relax recall, and relax F1-measure are 0.5957, 0.6774, 0.6335, 0.7847, 0.7133, and 0.7454 respectively. The results show the effectiveness of our proposed method. After deeply examining and analyzing the error cases, several clues cause incorrect alignments, such as, insufficient English definition of Chinese terms, a large number of redundant terms, incorrect word segmentations, excessive difference in the sentence separation between Chinese and English, and construction of Chinese-English sentence alignment is too complicated etc. The goal of this thesis is to design a practical sentence alignment approach between Chinses and English Buddhist texts to build parallel corpo
