Is there any standard or normal range for the amount of LSTM loss function?
Although your loss function is an indication of how well the model is training, usually one uses other more intuitive metrics to assess how good the model is.

If you are looking to a classification problem, your loss function is most probably the cross entropy. In what regards the loss function what matters is to understand its behaviour during training, more than its value.

A loss function that reduces its value during training is an indication that the model is effectively training. The loss function will, at some point, start reducing its value, and that means that the model has arrived to a minimum. One need to understand also the interaction of the loss function in training and validation set, and how to detect things like overfitting. If you are not aware of that, there is plenty of literature about the topic.

To know how good a model is, I would use other metrics that give a better indication and intuition. For example, in a classification problem, pne can look to things like Precision, Recall, Accuracy (if the classes are not very unbalance) or even ROC AUC. If it is a regression problem, maybe you are more interested in Mean or Median Absolute Percentage Error (MdAPE or MAPE).