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Software requirements specification is undoubtedly critical for the whole software life-cycle. Nowadays, writing software requirements specifications primarily depends on human work. Although massive studies have been proposed to fasten the process via proposing advanced elicitation and analysis techniques, it is still a time-consuming and error-prone task that needs to take domain knowledge and business information into consideration. In this paper, we propose an approach, named ReqGen, which can provide recommendations by automatically generating natural language requirements specifications based on certain given keywords. Specifically, ReqGen consists of three critical steps. First, keywords-oriented knowledge is selected from domain ontology and is injected to the basic Unified pre-trained Language Model (UniLM) for domain fine-tuning. Second, a copy mechanism is integrated to ensure the occurrence of keywords in the generated statements. Finally, a requirement syntax constrained decoding is designed to close the semantic and syntax distance between the candidate and reference specifications. Experiments on two public datasets from different groups and domains show that ReqGen outperforms six popular natural language generation approaches with respect to the hard constraint of keywords(phrases) inclusion, BLEU, ROUGE and syntax compliance. We believe that ReqGen can promote the efficiency and intelligence of specifying software requirements.