With the increasing number of Internet of Things （IoT） access devices， and the lack of awareness of the security of IoT devices of network management and maintenance staffs， attacks in IoT environment and on IoT devices spread gradually. In order to strengthen network security in IoT environment， an intrusion detection dataset based on IoT platform was used， the Convolutional Neural Network （CNN） + Long-Short Term Memory （LSTM） network was adopted as the model architecture， CNN was used to extract data spatial features， and LSTM was used to extract the data temporal features， the cross-entropy loss function was improved to a dynamic weight cross-entropy loss function， and an Intrusion Detection System （IDS） for IoT environment was produced. Experiments were designed and analyzed， and accuracy， precision， recall and F1-Measure were used as evaluation metrics. Experimental results show that compared with the model using traditional cross-entropy loss function， the proposed model using dynamic weight loss function under CNN-LSTM network architecture has an improvement of 47 percentage points in F1-Measure for Address Resolution Protocol （ARP） samples in the dataset， and has an improvement of 2 percentage points to 10 percentage points for other minority class samples in the dataset， which verifies the dynamic weight loss function can enhance the model’s ability to discriminate minority class samples， and this method can improve IDS’s ability to judge minority class attack samples.